{"as_of":"2026-08-20T00:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:16f7cdf9938e8c09ecad1bfb256c2a2d0480d3eeaf3905af5088a4bd8241c8b2","coverage":[{"denominator":84,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":84,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:41:09.807114Z","state":"measured"},{"denominator":85,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":85,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T18:58:25.713983Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":2,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"cited_work":{"arxiv_id":"2412.13948","doi":"10.48550/arxiv.2412.13948","metadata_source":"arxiv_reference","pith_arxiv_id":"2412.13948","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"64f9ddd1-399f-44cf-92e9-b47175c0f7b2","year":2024},"citing_paper":{"arxiv_id":"2606.19034","last_updated":"2026-06-17T13:05:08Z","snapshot_observed_at":"2026-08-15T08:36:05.082883Z","submitted_at":"2026-06-17T13:05:08Z","title":"Evaluating Learned Spatial Indexes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T18:58:25.713983Z"},"links":{"cited_paper":"/paper/2412.13948","citing_paper":"/paper/2606.19034"},"observation_digest":"sha256:a3519f8d743b5ec3d8bf8f9f1de80ffda981b82930d31dc408133ca4b8f1669d","observation_id":"a0568a32-8ff8-4608-99d6-aeef7fc91a5c","resolution":{"observed_at":"2026-06-26T18:59:43.333438Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.13948/citation-record","integrity":"/paper/2412.13948/integrity","json":"/paper/2412.13948/citation-record.json","paper":"/paper/2412.13948"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.332079Z","title":"Benchmarking Derivative-Free Optimization Algorithms","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.332079Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:b54d2e480da202b9939ad35af1bdce767d3df29390d4a823c22bdd1e5d2e787b","observation_id":"8c1c18d3-3fe9-4cc8-8bf9-76c20da99563","resolution":{"observed_at":"2026-08-11T12:41:09.332079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ces.2021","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.621483Z","title":"Data-Driven Optimization for Process Systems Engineering Applications","venue":null,"work_id":"467a8e50-ad1d-4914-83dc-efcb04a784b2","year":2022},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.337657Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:db679edbcd6c9580dc4766aa99ddf79e509bd17a2d175a7add226993500f769b","observation_id":"7bdff7e2-e2f9-4a64-806a-267acafabe6a","resolution":{"observed_at":"2026-08-11T12:41:10.626989Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.344353Z","title":"A Perspective on Smart Process Manufacturing Research Challenges for Process Systems Engineers","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.344353Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ef95664db6a58495f5552c86b0a35df022fc89264572c34b5b32d1db59d0dcbf","observation_id":"bb4788b9-cba9-45b6-9100-fd4a02724db5","resolution":{"observed_at":"2026-08-11T12:41:09.344353Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.349800Z","title":"Multi-Scale Optimization for Process Systems Engineering","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.349800Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:64866f8e194b87799e2dfc9eadaaaf6e97a2d14779027b42ffd4ef7e8c6f2df7","observation_id":"7f3dfa40-e6aa-44d8-881f-22661c492dac","resolution":{"observed_at":"2026-08-11T12:41:09.349800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.compchemeng.2017","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.585262Z","title":"The ALAMO Approach to Machine Learning","venue":null,"work_id":"7e215dde-df57-4642-b187-10115eb6550d","year":2017},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.355294Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:103e3e4877fd81493af5f2e11d83045d7eb99851d0542cf96c0c4fdf1b5a321c","observation_id":"b3bb9541-1cb2-400a-9161-e1b940a26b82","resolution":{"observed_at":"2026-08-11T12:41:10.592645Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.361575Z","title":"On the Numerical Performance of Finite-Difference-Based Methods for Derivative- Free Optimization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.361575Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:efa1bca03846eb17ccefbcbfff757ad7a966cb8164c7fe2b9ff47bec09d12eff","observation_id":"fe7e8c87-9abe-47fa-8328-2045729c0fb6","resolution":{"observed_at":"2026-08-11T12:41:09.361575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.367887Z","title":"Complete Search in Continuous Global Optimization and Constraint Satisfaction","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.367887Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:32926f2b2a8f3710c13055d760e46b80bcb72bff6ee7e9b0c44ca9e529e836f8","observation_id":"46e2921b-e9e6-4cdc-99f8-387559eaca02","resolution":{"observed_at":"2026-08-11T12:41:09.367887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.372866Z","title":"Advances in Surrogate Based Modeling, Feasibility Analysis, and Optimization: A Review","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.372866Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ab95e9bfdba323213f5ea8daa211975d8f26a56fc93e401c4ec5707d065e4f2f","observation_id":"66d3bda6-21dd-4ca2-83e8-0b4883eb10b7","resolution":{"observed_at":"2026-08-11T12:41:09.372866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11590-019-01428-7","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.538576Z","title":"Machine Learning-Based Surrogate Modeling for Data-Driven Optimiza- tion: A Comparison of Subset Selection for Regression Techniques","venue":null,"work_id":"87419d81-abee-4c0c-91f6-723c0d8a39a9","year":2020},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.377729Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ec9fcbb7119ad8fe0ac5530493e28429463faddde9f2622f1ee20fe354292002","observation_id":"39af475b-113f-454b-9297-63b3fb99755e","resolution":{"observed_at":"2026-08-11T12:41:10.545037Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.compchemeng.2018.10.007","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.519046Z","title":"Deterministic Global Process Optimization: Accurate (Single-Species) Properties via Artificial Neural Networks","venue":null,"work_id":"ef1db23a-3240-470a-bb57-a05bb5790db8","year":2019},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.382440Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:7836ea33d83cac35a4964d91be52249c7f0e802058179d0e053a93fb0aa07e35","observation_id":"6389b840-fa9f-4ab9-818e-943c2445a6ff","resolution":{"observed_at":"2026-08-11T12:41:10.525156Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02951","last_updated":"2024-04-03T18:00:00Z","snapshot_observed_at":"2026-08-16T14:03:52.215332Z","submitted_at":"2024-04-03T18:00:00Z","title":"Surrogate optimization of variational quantum circuits","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02951","snapshot_observed_at":"2026-08-11T12:41:09.389943Z","title":"Gustafson et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.389943Z"},"links":{"cited_paper":"/paper/2404.02951","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:6dc1deb4f5c60c03f441f778b5d6159201f4f0f799a1feb62a7b7c03976e67f1","observation_id":"52122769-dffd-49cf-8fea-2882b378c877","resolution":{"observed_at":"2026-08-11T12:41:09.389943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.18364","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"A Surrogate-based Framework for Feasibility-driven Optimization of Expensive Simulations","venue":"AIChE Journal","work_id":"56760d01-e566-4441-bd1e-e892e9826c68","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.396469Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:0547ee688e1498ca0f0f6f89f0b0a711ead221538ddc3fc835191e81ef16a244","observation_id":"fa076a43-db70-4b6d-afff-57ef4f6e532b","resolution":{"observed_at":"2026-08-11T12:41:10.505862Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.18110","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Algebraic Surrogate-based Process Optimization Using Bayesian Symbolic Learning","venue":"AIChE Journal","work_id":"bd08dcdb-2d4d-4b63-b00e-655757b1b497","year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.401853Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:1304b6d7f594be0501dfcd2aa3412e515f1bffeea6aef620e898561b1cd862b2","observation_id":"3b5bd02c-9fc8-43ed-a5ce-32872a025da9","resolution":{"observed_at":"2026-08-11T12:41:10.480171Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.413211Z","title":"Surrogate-Based Optimisation of Process Systems to Recover Resources from Wastewater","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.413211Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:f69a3ca713e7b61bc7fa82a466bd5d85520e7ec3794565a48aa542b7e0aadc74","observation_id":"e7bb7126-e3e0-4e70-9a3a-58b9bae21fb7","resolution":{"observed_at":"2026-08-11T12:41:09.413211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.16373","last_updated":"2024-01-29T18:12:32Z","snapshot_observed_at":"2026-08-16T14:23:32.876601Z","submitted_at":"2024-01-29T18:12:32Z","title":"Bayesian optimization as a flexible and efficient design framework for sustainable process systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16373","snapshot_observed_at":"2026-08-11T12:41:09.423440Z","title":"Paulson and Calvin Tsay","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.423440Z"},"links":{"cited_paper":"/paper/2401.16373","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:28f81b11212513fe8c0986970e0a53e8365737b6bf61a98b76074961749ffdef","observation_id":"218d1c5c-9e47-480d-9bfd-8f5fcc4654f5","resolution":{"observed_at":"2026-08-11T12:41:09.423440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.10687","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.418535Z","title":"Surrogate Based Optimization of a Process of Polycrystalline Silicon Production","venue":null,"work_id":"cf1e5f99-1a4e-4aee-8f66-aff3a62ffc08","year":2020},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.428916Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:3a8ec774c27d34f27f1b2bc64d46a61cac561f57ef7eee2b4f3a946ada3f9d6b","observation_id":"88eae16d-7cf8-4bd8-8f3e-b901ff60a6e7","resolution":{"observed_at":"2026-08-11T12:41:12.435958Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.434750Z","title":"A Trust Region Framework for Heat Exchanger Network Synthesis with Detailed Individual Heat Exchanger Designs","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.434750Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:e69a9104ed762ca295847f819186182588fca4cf6273e24e60f1acc39f0e418c","observation_id":"01b039ef-f15c-4976-ba97-844f3cf17c2f","resolution":{"observed_at":"2026-08-11T12:41:09.434750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1021/acs.iecr.4c00692","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.428807Z","title":"Integrating Graph Neural Network-Based Surrogate Modeling with Inverse Design for Granular Flows","venue":null,"work_id":"86f2b33f-31a0-48d6-9e97-3c8d236fc94c","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.439717Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:37d6c0d2fd3f01e1258d12f4259f26681efe21650e8a371f972c1e41da5f2198","observation_id":"c41c184a-9d56-4d93-b5f8-1a2f05de6705","resolution":{"observed_at":"2026-08-11T12:41:10.435143Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.10863","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.180384Z","title":"Algebraic Surrogate-Based Flexibility Analysis of Process Units with Complicating Process Constraints","venue":null,"work_id":"bb552eee-8686-4a0f-9416-f9943e8ab30e","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.444799Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:a127ff84d6c0c70e8f3611b57c2fca4c04bdbc34fa24420f2dff146a791ae621","observation_id":"313e49c2-937e-4f7f-92a4-af3d59478781","resolution":{"observed_at":"2026-08-11T12:41:12.191958Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.11707","last_updated":"2024-03-18T12:12:23Z","snapshot_observed_at":"2026-08-16T14:08:50.203832Z","submitted_at":"2024-03-18T12:12:23Z","title":"A Quantile Neural Network Framework for Two-stage Stochastic Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.11707","snapshot_observed_at":"2026-08-11T12:41:09.449840Z","title":"A Quantile Neural Network Framework for Two-stage Stochastic Optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.449840Z"},"links":{"cited_paper":"/paper/2403.11707","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:0e7027fd9e86b7a4094941cfad72419b5feb646fc86360fc23b05c87248951bd","observation_id":"9a4515e3-2905-40f2-957b-3fc44e5e2ded","resolution":{"observed_at":"2026-08-11T12:41:09.449840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/cite.202000025","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Hybrid Semi-parametric Modeling in Separation Processes: A Review","venue":"Chemie Ingenieur Technik","work_id":"fadc883f-2184-42f1-9c5d-3da8a273384e","year":2020},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.455329Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:561370c43fbe85ea478bfd6fd267b75022caa5221df5b1876da7558fb9ecec24","observation_id":"2db885b8-7369-4cf1-8245-e2a9ac7c7dae","resolution":{"observed_at":"2026-08-11T12:41:10.416891Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/b978-0-444-64241-7.50207-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.393696Z","title":"Data-Driven Models and Algorithms for Demand Response Scheduling of Air Separation Units","venue":null,"work_id":"d163bba4-9536-46a8-9208-1f80d0bb01b9","year":2018},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.460429Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:dfc086b840968c1789eb449a40d3b34791960b1b29b1155a9ff759dae7dcb6ba","observation_id":"eb3fd48a-b9e6-4705-93dd-df1ff012a726","resolution":{"observed_at":"2026-08-11T12:41:10.399752Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.465113Z","title":"Data-Driven Optimization of Mixed- Integer Bi-Level Multi-Follower Integrated Planning and Scheduling Problems under Demand Uncertainty","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.465113Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:2de6c28f2a11b8181934022e18ff61087af5c6367dc89b1ca2261836fd8b33b5","observation_id":"3a0add96-bcb8-4dab-b70a-693d1fb4f355","resolution":{"observed_at":"2026-08-11T12:41:09.465113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11081-015-9288-8","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-19T11:03:45.297681Z","title":"Data-Driven Construction of Convex Region Surrogate Models","venue":"Optimization and Engineering","work_id":"01490cf9-cafc-40e5-9ea6-1484687b9bc4","year":2016},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.470932Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:59adc0ca4ea3d5653a6fea093c460454908002e5c235c4565773d9e713113823","observation_id":"2d439953-d643-4405-8d2d-45350101b458","resolution":{"observed_at":"2026-08-11T12:41:10.382084Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.476632Z","title":"Data-Driven Strategies for Optimization of Integrated Chemical Plants","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.476632Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:98fe5e5315ad18fe16929348d5c1f86a213de15a70668f17ee5aac5e7a9d1d0c","observation_id":"aaef2391-3eee-4bfd-b245-a2793cb7be8c","resolution":{"observed_at":"2026-08-11T12:41:09.476632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07870","last_updated":"2023-10-11T20:21:34Z","snapshot_observed_at":"2026-08-16T14:52:54.670589Z","submitted_at":"2023-10-11T20:21:34Z","title":"Hierarchical planning-scheduling-control -- Optimality surrogates and derivative-free optimization","version":1},"cited_work":{"arxiv_id":"2310.07870","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.07870","snapshot_observed_at":"2026-08-11T12:41:11.813189Z","title":"Hierarchical planning-scheduling-control -- Optimality surrogates and derivative-free optimization","venue":"math.OC","work_id":"abc96759-d1c0-4369-bf56-5ccb6c00ec5a","year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.481652Z"},"links":{"cited_paper":"/paper/2310.07870","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:eeda2eae39f4de4eda117dc3ee6e8bc6a6d6241aee544ee7f752530807bdf028","observation_id":"aed10bd2-2371-4fb8-9aef-51b56915f7dc","resolution":{"observed_at":"2026-08-11T12:41:11.820782Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.488350Z","title":"High-Throughput Screening of Catalytically Active Inclusion Bodies Using Laboratory Automation and Bayesian Optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.488350Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:50fc8309fa9ab0384aa23b5dadb065293a1a1d679b51ea4d604facac516e2449","observation_id":"28b11b84-5247-4b03-947f-7e739cd37d1f","resolution":{"observed_at":"2026-08-11T12:41:09.488350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1021/jacs.2c06833","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.323407Z","title":"Into the Unknown: How Computation Can Help Explore Uncharted Material Space","venue":null,"work_id":"2e7480c7-44d3-4cf3-96d8-29c2db701cae","year":2022},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.493976Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:94d902f9f19b9b39d83bf65106a59d59a7e9c969ea2389989e18dc91e589cb93","observation_id":"b7de77a3-8e50-412a-8101-f99c7b3c3eeb","resolution":{"observed_at":"2026-08-11T12:41:10.330503Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1039/d3re00502j","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.295467Z","title":"Multi-Objective Bayesian Optimisation Using q -Noisy Expected Hypervolume Improve- ment ( q NEHVI) for the Schotten–Baumann Reaction","venue":null,"work_id":"2f9c6ad6-dd50-4e92-b063-62b9bf529e07","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.499264Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:5b420171fce97da414ae786fca5b5192ee5657781f76b8d24b8e8bc5098b68a5","observation_id":"894e7a2e-edee-46de-a4a3-5e40c3ba32bd","resolution":{"observed_at":"2026-08-11T12:41:10.301452Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.504498Z","title":"Discrete and Mixed-Variable Experimental Design with Surrogate-Based Approach","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.504498Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:e5d92491ebdf5c9031dde08e9380c16608e978efcdfb1d1ed26b08770c513c18","observation_id":"004a759f-5759-4f73-b157-523369f6209b","resolution":{"observed_at":"2026-08-11T12:41:09.504498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.514285Z","title":"Stochastic Data-Driven Model Predictive Control Using Gaussian Processes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.514285Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:84712081e354cf6e1e996125d5afa6d3394b46440a437eca161e45d20e5c68ca","observation_id":"16108bde-eefe-4032-8572-dd281f87fb4c","resolution":{"observed_at":"2026-08-11T12:41:09.514285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.29995","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:11.765892Z","title":"Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning","venue":null,"work_id":"f4c65c62-a5c3-4f5a-b1f4-b54168bc3735","year":2020},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.519905Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:4fd339f788a5fd4c0393f326a769d50db3b986b57de87cd3626e2040c7af0706","observation_id":"071fa1c8-f4ec-431a-9fe7-379f3776868c","resolution":{"observed_at":"2026-08-11T12:41:11.780649Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.18428","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"A Data-driven Bayesian Approach for Optimal Dynamic Product Transitions","venue":"AIChE Journal","work_id":"50ea0292-57ed-48bf-ac8b-038223e75501","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.525213Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:637e56e4c7db175b5ddf92b73838d8bb112f0c69e3a7243832c735bfef49cbc6","observation_id":"85b324dc-b25d-463a-963a-2a5d9aca2fa7","resolution":{"observed_at":"2026-08-11T12:41:10.251691Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jprocont.2022.12.001","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.228581Z","title":"Online Feedback Optimization of Compressor Stations with Model Adaptation Using Gaussian Process Regression","venue":null,"work_id":"3a59a743-3214-4f3b-b51e-d56d8e4e3683","year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.530857Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ad9455468662b4a329037f77b91bf375858c3d9cafba7c74ad3e4cf3bbb2d0b2","observation_id":"a9aa5e37-1d15-4949-9f78-2a73a79ed58a","resolution":{"observed_at":"2026-08-11T12:41:10.234076Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.537099Z","title":"A Data-driven Optimization Algorithm for Differential Algebraic Equations with Numerical Infeasibilities","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.537099Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:bd5dcc2e8f593716f15a7b0fd8b6649c4cfadc4c79b57ccef2813a8c9145e7d2","observation_id":"65155936-0501-4c82-a218-94150ede8ca8","resolution":{"observed_at":"2026-08-11T12:41:09.537099Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.544551Z","title":"Data-driven Decision-focused Surrogate Modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.544551Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:21b7fc72cf5936b60178fb2a58131aa1483f4c2e8983a7f4c1b746daae4140a1","observation_id":"4331ceaa-0e46-4914-bbb0-f19bf727b4bc","resolution":{"observed_at":"2026-08-11T12:41:09.544551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04686","last_updated":"2024-12-11T12:06:10Z","snapshot_observed_at":"2026-08-16T15:56:31.767267Z","submitted_at":"2023-02-09T15:04:35Z","title":"Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates","version":4},"cited_work":{"arxiv_id":"2302.04686","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.04686","snapshot_observed_at":"2026-08-11T12:41:11.669633Z","title":"Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates","venue":"math.OC","work_id":"0c092011-9025-4328-9db1-7039dbccacfc","year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.552017Z"},"links":{"cited_paper":"/paper/2302.04686","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:a315da2155beb69d7bbb141ee9289840c7b0c5757cb315ca69c3a6982f0a085f","observation_id":"738adada-67c6-4bf8-86ee-1eace0792e31","resolution":{"observed_at":"2026-08-11T12:41:11.675359Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11081-022-09740-5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-19T11:03:45.297681Z","title":"Surrogate-Based Branch-and-Bound Algorithms for Simulation-Based Black-Box Optimization","venue":"Optimization and Engineering","work_id":"248c907f-ddb1-4775-aa38-53584637297a","year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.558919Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:e5409d8223da5b75feb251cefc204af1a233d9cb0282ed575bc858e235587c81","observation_id":"71f3e198-371a-4ab4-b931-5462d202efb4","resolution":{"observed_at":"2026-08-11T12:41:10.207139Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/cjce.25512","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Assuring Optimality in Surrogate-based Optimization: A Novel Theorem and Its Practical Implementation in Pressure Swing Adsorption Optimization","venue":"The Canadian Journal of Chemical Engineering","work_id":"a3b727cd-3e4a-4f58-80fd-87a36326e6ab","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.564338Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:2708f105326f2624c7af80b69fb7a72552a97eb67dc634c43a9e26c9ee7f7078","observation_id":"debafa65-aaf4-40f9-9386-70d033752008","resolution":{"observed_at":"2026-08-11T12:41:10.188635Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.18448","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"A Bayesian Optimization Approach for Data-driven Mixed-integer Nonlinear Programming Problems","venue":"AIChE Journal","work_id":"cfc6fa99-1a14-4855-91b8-c481bb40aaf6","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.570012Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:f6b0f0ff0ebb2d779104d304eacbe29788939242dff9340d37b4bef445c23979","observation_id":"1d7a9153-b2bf-4a9e-a29b-d8369a8f29bb","resolution":{"observed_at":"2026-08-11T12:41:10.165726Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.17977","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Data-driven Coordination of Subproblems in Enterprise-wide Optimization under Organizational Considerations","venue":"AIChE Journal","work_id":"c4c5c31b-2bb2-4e26-bb11-a8a0c680a8db","year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.575683Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:f218953223a08fd595b6a9137d6282c1dbfab3e1d08fde4c9e6648306f4cc220","observation_id":"02890a56-1494-479d-a88d-7f106f92ae27","resolution":{"observed_at":"2026-08-11T12:41:10.146898Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.580967Z","title":"Derivative-Free Optimization Methods","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.580967Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ec6390a861a262f8dca76dda4233847b0961bfa63eed94d6d9273dffa3c202f8","observation_id":"3e2fcc79-c95d-421e-af78-7d65268ba791","resolution":{"observed_at":"2026-08-11T12:41:09.580967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.11585","last_updated":"2019-06-25T18:54:52Z","snapshot_observed_at":"2026-08-19T15:53:42.645738Z","submitted_at":"2019-04-25T20:58:33Z","title":"Derivative-free optimization methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.11585","snapshot_observed_at":"2026-08-11T12:41:09.585925Z","title":"Derivative-Free Optimization Methods","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.585925Z"},"links":{"cited_paper":"/paper/1904.11585","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:48eac50d646f2dc9bc86a20535bf1d126f33b4ca301d05337e23f7da74b127b1","observation_id":"a4d419d7-ee00-4774-9f58-fb10f3f858f8","resolution":{"observed_at":"2026-08-11T12:41:09.585925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.592269Z","title":"Model-Based Derivative-Free Optimization Methods and Software","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.592269Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:743d47518d63ce43a25f78e95f1a15edc26aef86ff53d1db8e0bc68b8f0bca5b","observation_id":"41245cc0-b5d0-4196-b1af-6e7511082cf9","resolution":{"observed_at":"2026-08-11T12:41:09.592269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:13.028878Z","title":"GPyOpt: A Bayesian Optimization Framework in Python","venue":null,"work_id":"dee0458c-cb49-4f34-acfd-7976d28242d4","year":2016},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.597291Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:b4044cbb9666332cd1728d8296acd94a424e49c18599ed6f6b49608cc625fc0a","observation_id":"db117555-55ab-452c-af56-5bfd054c33ab","resolution":{"observed_at":"2026-08-11T12:41:13.036536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.602071Z","title":"SOP: Parallel Surrogate Global Optimiza- tion with Pareto Center Selection for Computationally Expensive Single Objective Problems","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.602071Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:6a38da3d8c865a0b8ddfbc28c5561d64f03eda2065bd68e757b5b7357fae7f60","observation_id":"f0cbbdc5-37db-4ddd-b4d7-e8a91cad460f","resolution":{"observed_at":"2026-08-11T12:41:09.602071Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1060.0182","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:11.611486Z","title":"A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions","venue":null,"work_id":"4c0af32e-6662-4655-b8a0-ff79ea586883","year":2007},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.607948Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:80e0f54be357e35df88404c1a8a7000cb1ce0c2141fd307bd147bded92963dc1","observation_id":"1d198f1f-0bb3-417f-ad0b-a35c0a24807f","resolution":{"observed_at":"2026-08-11T12:41:11.625526Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.612712Z","title":"ENTMOOT: A Framework for Optimization over Ensemble Tree Models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.612712Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:cf17e932fb33bb616f9a3ed562f08b13b060f80159dccd4e7da1f58d0ddc8a44","observation_id":"f6cf46cb-8447-4c80-bdf6-2b134ed3f85c","resolution":{"observed_at":"2026-08-11T12:41:09.612712Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7612.13776","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:11.398674Z","title":"SNOBFIT – Stable Noisy Optimization by Branch and Fit","venue":null,"work_id":"2261eee1-d815-458f-a71a-f9a8356dff48","year":2008},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.617443Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:08a4eb3a2878d4841728e7c0096aa6f036690f9daf41458393f00b83719b35fd","observation_id":"475f7b7b-aa3a-44b5-b23d-942284a4275c","resolution":{"observed_at":"2026-08-11T12:41:11.411001Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.621684Z","title":"ARGONAUT: AlgoRithms for Global Optimization of coNstrAined Grey-Box compUTational Problems","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.621684Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:a6b207a738c63264ee301edf6a4d9e84383c067f0b496285540b05962a6232f2","observation_id":"4a1ed6fc-3cec-4327-b52d-45795f8095db","resolution":{"observed_at":"2026-08-11T12:41:09.621684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.12341","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Surrogate-based Superstructure Optimization Framework","venue":"AIChE Journal","work_id":"d75de63c-8e29-4011-909b-30c8069e3180","year":2011},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.626026Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:f39fb615c2a7b5e218a0678c8efe3ec17ee15e72c7d0eca5dc459d9b53d90033","observation_id":"b9a2446c-ef3a-4c69-9483-fb606fdebb7d","resolution":{"observed_at":"2026-08-11T12:41:10.087909Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.631039Z","title":"Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.631039Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:b277b74f4bba3500beaee96d4a497900a81efcca8df6e640a8e671a9fda48be3","observation_id":"ee09362b-c371-4b81-9e30-e0eaf837709c","resolution":{"observed_at":"2026-08-11T12:41:09.631039Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.11579","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"An Algorithm for the Use of Surrogate Models in Modular Flowsheet Optimization","venue":"AIChE Journal","work_id":"46651ddf-ce64-4794-bc50-6e32db732163","year":2008},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.635411Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ea0bbe900d77ae3442ae1e639d6f86393d3c55859c441018caaeb9e9479e8ae3","observation_id":"6e7d1d92-b646-413d-a1a1-b62eaa6698a3","resolution":{"observed_at":"2026-08-11T12:41:10.454342Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.10724","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:11.223517Z","title":"Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation","venue":null,"work_id":"e36516c6-b81c-444c-90d6-bc5630d11e5d","year":2021},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.639904Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:b7fda6b81a591b518942fef9ae9f8f617a4d9e56f9ee6f34cf153af3321ef08b","observation_id":"6cc3a677-0087-43bb-87e6-8be1ae77d54a","resolution":{"observed_at":"2026-08-11T12:41:11.234364Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1023/a:1011255519438","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.061620Z","title":"A Radial Basis Function Method for Global Optimization","venue":null,"work_id":"cafceef8-a1d1-498c-97e6-4b5695ea74be","year":2001},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.644274Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ff865aa09cba16fa7ba355fe0b1abe585a5448abfc1940e8e5f5b51ff326979b","observation_id":"a445f096-f688-4315-964d-987886f19e4b","resolution":{"observed_at":"2026-08-11T12:41:10.067081Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s12532-018-0144-7","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.042616Z","title":"RBFOpt: An Open-Source Library for Black-Box Optimization with Costly Function Evaluations","venue":null,"work_id":"0b26c006-fc0c-49f1-969d-885df4684ccb","year":2018},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.649553Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:6b81f15d6db5a240f3027f3cf80f1926ff247f4b58340c530a1a4294d722e46e","observation_id":"808224dc-dcc8-42de-96b7-3041bee60548","resolution":{"observed_at":"2026-08-11T12:41:10.048360Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.654053Z","title":"Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.654053Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:a614a69512a6664d0971dde6f1abe35d4960923c3d303a4abab1a1d8b724dd0e","observation_id":"87d72e1e-8840-4d0e-bbd0-efae4672e979","resolution":{"observed_at":"2026-08-11T12:41:09.654053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.659014Z","title":"A Progressive Barrier Derivative-Free Trust-Region Algorithm for Constrained Opti- mization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.659014Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:662967ffad5ab3a0d725bc6e26e460e731dcaeeffcffd34a837ef11edd85477b","observation_id":"7ce161e0-59d1-40a5-82ca-8bc0e7f1f8b4","resolution":{"observed_at":"2026-08-11T12:41:09.659014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:13.003139Z","title":"Bayesian Optimization with Inequality Constraints","venue":null,"work_id":"ca22f978-7447-414b-a243-0112a1e8e3e3","year":null},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.664659Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:790e853902ab8f9a998496cfc56615108d120ee7113b06c58e69ae60d9d68ed6","observation_id":"b1b2f7a4-1360-4dde-b413-81290f7539ea","resolution":{"observed_at":"2026-08-11T12:41:13.010099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.978522Z","title":"Derivative-free Optimization for Expensive Constrained Problems Using a Novel Expected Improvement Objective Function","venue":null,"work_id":"2b6fc7d1-39dd-4339-9dd3-571f51b3222e","year":2014},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.670034Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:514632a986ef5b360982dae98f947a29c03ef6c265f4c8c37ec8ae0ef046bb37","observation_id":"78ca1bd9-226b-44e7-882a-cc44b52739bf","resolution":{"observed_at":"2026-08-11T12:41:12.985281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.16364","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Advanced Trust Region Optimization Strategies for Glass Box/ Black Box Models","venue":"AIChE Journal","work_id":"d7588c71-2be0-47f8-ad5a-9b01895d642b","year":2018},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.675835Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:92a1bd1b8cd9542250c474d3f472e4469b44c4d493d545be4833fbbc749bb3da","observation_id":"0f0fa86b-600c-4bf5-bdb8-536739a94b0e","resolution":{"observed_at":"2026-08-11T12:41:10.006505Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.96835","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:11.138172Z","title":"Safe Real-Time Optimiza- tion Using Multi-Fidelity Gaussian Processes","venue":null,"work_id":"b3ebcd5c-1332-4a80-841b-a15ad17a3887","year":2021},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.681091Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:11ed0cf73353abd59df45985bac1d5cbf7beb80f445e169adde557d50742ca84","observation_id":"ebdff3a8-077e-466a-b32d-716fbfcff160","resolution":{"observed_at":"2026-08-11T12:41:11.147144Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.954843Z","title":"Dimensionality Reduction for Production Optimization Using Polynomial Approximations","venue":null,"work_id":"2033905d-2ddf-4483-ba8a-40c33e6ad70e","year":2017},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.687282Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:b25127d38d6decb9732e1cc1b55e07e11b5c94f126038e9af0b14a468f7d3a12","observation_id":"c3686fd6-c84e-4eef-bc44-2383bdb04328","resolution":{"observed_at":"2026-08-11T12:41:12.962066Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.933995Z","title":"Batch Bayesian Optimization via Local Penalization","venue":null,"work_id":"8c56b120-85ed-47d5-82d8-2bd1aee29ff1","year":null},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.693161Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:625ddf7566e151ab0987d5978af9c60a0885921d8f06e63327d4b3766507910a","observation_id":"79c98de6-eb67-4dbb-aafe-0fcd5ab169b1","resolution":{"observed_at":"2026-08-11T12:41:12.940969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1505.01627","last_updated":"2015-05-07T08:50:18Z","snapshot_observed_at":"2026-08-19T22:00:05.954298Z","submitted_at":"2015-05-07T08:50:18Z","title":"Bayesian Optimization for Synthetic Gene Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.01627","snapshot_observed_at":"2026-08-11T12:41:09.699711Z","title":"Bayesian Optimization for Synthetic Gene Design","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.699711Z"},"links":{"cited_paper":"/paper/1505.01627","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:28907d054bb6ee6513f57af5b21302c3243d9d9bd1cd90b618b2e142bb3ab8bb","observation_id":"de754598-53c2-4aa9-8485-4cc6c6ee0af2","resolution":{"observed_at":"2026-08-11T12:41:09.699711Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.903239Z","title":"GLASSES: Relieving The Myopia Of Bayesian Optimisation","venue":null,"work_id":"c223b7d2-600f-4925-a8cf-98255556bc95","year":null},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.705693Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ba41081546aadd43a8e2d50ea47b39303bd98fbaa4f2d0e5e9758d561c7a5800","observation_id":"4e5f808e-ae7c-4b4b-839b-62dadc478776","resolution":{"observed_at":"2026-08-11T12:41:12.909821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.880741Z","title":"Scalable Global Optimization via Local Bayesian Optimization","venue":null,"work_id":"07831026-6a37-4a59-8811-520009367356","year":2019},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.713666Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:ed3f51e8934d671fb679b8d22d0299435a2388d391d61faca12f6a5f572210af","observation_id":"106fc02a-969b-452c-aec2-bb678736422b","resolution":{"observed_at":"2026-08-11T12:41:12.888360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.853478Z","title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","venue":null,"work_id":"89dd06f0-e13a-49f3-8dcf-3885482717cd","year":null},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.720044Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:6637ab12c3338d490aee982d36c53ec1ed6a9526a2eef6e0c68ad01be2257715","observation_id":"c240cc67-64c9-4dcc-8ef2-0773fc7ad707","resolution":{"observed_at":"2026-08-11T12:41:12.861773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.831974Z","title":"Classification And Regression Trees","venue":null,"work_id":"2000af54-cbd7-48b6-81cc-ffb69aea4073","year":1984},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.725520Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:58b514c3b9b1934796bddd44311dbdef23e690aa6cec446dbc195b31f0225397","observation_id":"490eb672-35f0-417f-a7a3-9cdb553027b4","resolution":{"observed_at":"2026-08-11T12:41:12.838119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1098/rsbm","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:11.016443Z","title":"Michael J. D. Powell. 29 July 1936—19 April 2015","venue":null,"work_id":"17f55645-c0b0-41f2-b854-9de0b8c48b07","year":1936},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.731308Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:a7f6b3a59072c8b2bc7f6ddb955a843c69138d6dac5ac9079b7206f786a45b8a","observation_id":"0527f264-976d-43a3-9575-6a02dae45220","resolution":{"observed_at":"2026-08-11T12:41:11.022118Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.737519Z","title":"A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.737519Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:2bfe2bbc0f3a052530ab922245c28b37bc42bf3427d557011bf450db2366892f","observation_id":"59eaeee9-8d56-49b2-85bd-3971c8293132","resolution":{"observed_at":"2026-08-11T12:41:09.737519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.00943","last_updated":"2016-06-01T04:56:04Z","snapshot_observed_at":"2026-08-14T22:07:45.406332Z","submitted_at":"2016-03-03T01:07:38Z","title":"CVXPY: A Python-Embedded Modeling Language for Convex Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.00943","snapshot_observed_at":"2026-08-11T12:41:09.744791Z","title":"CVXPY: A Python-Embedded Modeling Language for Convex Optimization","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.744791Z"},"links":{"cited_paper":"/paper/1603.00943","citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:0b9696ee5fdde11a3efd80172e083e3b0504a9b2890f4805faa0571c32b25a0d","observation_id":"e9fad459-d5ba-496a-9c91-d27156d6d2d5","resolution":{"observed_at":"2026-08-11T12:41:09.744791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/b978-0-443-28824-1.50533-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.957901Z","title":"High-Dimensional Derivative-Free Opti- mization via Trust Region Surrogates in Linear Subspaces","venue":null,"work_id":"a1e6b9d9-43c2-42df-9c11-14a267befd4f","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.750626Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:02c39ed5b9b6868f4fcedf45c6dd5ea112e3c431b7a5f9405e56233ec2efb457","observation_id":"0c2b55a8-3ee9-4d87-b9df-a1cbe70becae","resolution":{"observed_at":"2026-08-11T12:41:09.966396Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.756435Z","title":"Combining Radial Basis Function Surrogates and Dynamic Coordinate Search in High-Dimensional Expensive Black-Box Optimization","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.756435Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:af96b7fd291871530ee1cd1c30dbcd736b907c6c88fba2d83912cced471c63f7","observation_id":"b45e9ed3-5141-4d0b-a9e4-30da1f773b63","resolution":{"observed_at":"2026-08-11T12:41:09.756435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.809848Z","title":"A Radial Basis Function Method for Global Optimization","venue":null,"work_id":"ff240f8b-0187-49ab-8766-06656f20c4b1","year":null},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.762474Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:c712df85a24780b681b0d1e713fb876cbab12c9ee360ab8ed2d20452c413ca00","observation_id":"7ed97410-2f34-4f18-84a6-ffe933885649","resolution":{"observed_at":"2026-08-11T12:41:12.815305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1090.0325","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.782171Z","title":"Parallel Stochastic Global Optimization Using Radial Basis Functions","venue":null,"work_id":"f773f732-2e76-4bd3-a5d7-535e18012a68","year":2009},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.768670Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:4748bec3e78724e555a302d93d71754175d312a1f5d4cb8d955873d7fbf27f00","observation_id":"b11a5298-9bf6-4eb4-81da-480bfbb0ba8b","resolution":{"observed_at":"2026-08-11T12:41:10.793780Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.773673Z","title":"Dynamically Dimensioned Search Algorithm for Computationally Efficient Watershed Model Calibration","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.773673Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:599f37cd52e0c92ecee533226874ef6bfa896a1cbb1664e5241828362285c7ba","observation_id":"2ca19155-3fa8-4496-b242-cd64d6ca49aa","resolution":{"observed_at":"2026-08-11T12:41:09.773673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.778719Z","title":null,"venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.778719Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:a8ebdde66dc6ba53b73458ab0a1029d1e74c456cab7608a85fa261fdaf407d56","observation_id":"beec00b8-2933-4a48-860d-7983ac41be20","resolution":{"observed_at":"2026-08-11T12:41:09.778719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.787798Z","title":"Le mouvement brownien","venue":null,"work_id":"c2312791-1ec9-42ea-9354-cca7add8178f","year":1954},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.783871Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:041e23ab7dfa7f9244af80732cf750237c8bcf5acaeed74d879d86dcf963b510","observation_id":"9c018586-7c37-41b2-9151-3f7949acb168","resolution":{"observed_at":"2026-08-11T12:41:12.795716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/comjnl/3.3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:09.887229Z","title":"An Automatic Method for Finding the Greatest or Least Value of a Function","venue":null,"work_id":"d1347d56-5371-4602-b03e-8030bf9c3b7d","year":1960},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.789116Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:bc09d34fcfff436157d9d1d8245281bd1f2c67b48d6f4a1534e871fa9b7e1ab1","observation_id":"acd8f160-2f16-47f0-8e73-ba0ecd4fa8e6","resolution":{"observed_at":"2026-08-11T12:41:09.905020Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.744394Z","title":"Random Optimization","venue":null,"work_id":"d3a01012-a71c-4744-9cdb-10d7fca62e5c","year":1965},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.801765Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:f5df21c64c0e0dd8dfc16811210d9c634d7d130dd5348c55110bd717efddddfe","observation_id":"1990f3f8-3fa4-4e94-aa87-c10eefdac5ae","resolution":{"observed_at":"2026-08-11T12:41:12.751079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/cjce.22402","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:03:20.376878Z","title":"Assessing the Reliability of Different Real-time Optimization Methodologies","venue":"The Canadian Journal of Chemical Engineering","work_id":"cc5f03eb-18ad-4cc0-a2d9-0051fd583c49","year":2016},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.807114Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:006d86f4b52d420a3b3c4e75896a2b9ddcfb1e7ec642ed3f370f20b478c6dd0d","observation_id":"2a632e0e-0e88-40dd-b711-d66937316306","resolution":{"observed_at":"2026-08-11T12:41:09.870817Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:12.766978Z","title":null,"venue":null,"work_id":"902334a4-aa54-41eb-a3d3-adc6f91e82bc","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":175,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.795028Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:3e6b1e88245bea06c75bd2e9618c3343b70d59d37fbe7e8206a0310fb115a917","observation_id":"1e39d333-31c4-4e19-bbb7-58155aa37ad4","resolution":{"observed_at":"2026-08-11T12:41:12.772167Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.26434/chemrxiv-2024-h37x4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:41:10.276927Z","title":"(Visited on 07/01/2024)","venue":null,"work_id":"c4c3a54c-926b-410b-8be5-bb3fd6aef0bb","year":2024},"citing_paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T12:41:09.509501Z"},"links":{"citing_paper":"/paper/2412.13948"},"observation_digest":"sha256:4d7fc3038a6921ecb3b53d22359094ef95d8bb8acff42f21933c586a2b3a5e13","observation_id":"7789cf71-3641-4baf-9d4d-f18b3d40e554","resolution":{"observed_at":"2026-08-11T12:41:10.282706Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.13948","last_updated":"2024-12-18T15:29:05Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-16T10:03:22.629864Z","submitted_at":"2024-12-18T15:29:05Z","title":"Surrogate-Based Optimization Techniques for Process Systems Engineering"},"reference_resolution":{"displayed":84,"state_counts":{"malformed_identifier":7,"metadata_mismatch":6,"parse_uncertain":1,"unresolved":28,"verified_exact":31,"verified_fuzzy":11},"total_outbound_references":84},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 1 inbound Pith citation observation for arXiv:2412.13948."}