{"as_of":"2026-08-08T17:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5ef618c337cb874ddba91e55a2d577e8e516ec3137e6878168bb10fd66cbf50b","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T00:38:19.391460Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2511.00366/citation-record","integrity":"/paper/2511.00366/integrity","json":"/paper/2511.00366/citation-record.json","paper":"/paper/2511.00366"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T00:38:14.961246Z","title":"Reengi- neering aircraft structural life prediction using a digital twin.International Journal of Aerospace Engineering, 2011(1):154798, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:14.961246Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:ecae1598b84db4e3a4951b1fac839e903379b1139ae80a0e456c1e7551d8f96b","observation_id":"a58dfc93-7da8-4e66-bf18-2ef44c857691","resolution":{"observed_at":"2026-08-04T00:38:14.961246Z","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":"2026-08-04T00:38:15.053877Z","title":"The digital twin paradigm for future nasa and us air force vehicles","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.053877Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:74e6236dfc63112b954c9efc624169cf356a5267764f10e6410a97ac8a4a1305","observation_id":"a19a51ab-7a13-4d7b-b3eb-0a9f123ea75c","resolution":{"observed_at":"2026-08-04T00:38:15.053877Z","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":"2026-08-04T00:38:15.186463Z","title":"Grieves.Digital Twins: Past, Present, and Future, pages 97–121","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.186463Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:83d4385ce35689e403f1d7d57f580bf721820a78ff70e9982990100208ebe066","observation_id":"df49120e-97c7-4021-a609-b71b30f1c3db","resolution":{"observed_at":"2026-08-04T00:38:15.186463Z","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":"2026-08-04T00:38:15.299801Z","title":"Data-driven physics-based digital twins via a library of component-based reduced-order models.International Journal for Numerical Methods in Engineering, 123(13):2986–3003, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.299801Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:b5ba9d8ef2f89a6b60ecf31c3316008312db494685bf519658a5293a24315b30","observation_id":"e35ffdc9-722d-4d9e-ab93-9fcfdcc829e3","resolution":{"observed_at":"2026-08-04T00:38:15.299801Z","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":"2026-08-04T00:38:15.428946Z","title":"On the effects of modeling as-manufactured geometry: Toward digital twin.International Journal of Aerospace Engineering, 2014(1):439278, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.428946Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:b4c311319b002c481c3acf4e4fac7c2e28eb58b5b9505b0d9ad76b2a4561f5f7","observation_id":"46297ff8-92e4-4aac-ba39-796e7b523f18","resolution":{"observed_at":"2026-08-04T00:38:15.428946Z","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":"2026-08-04T00:38:15.549016Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.549016Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:089469364c8c3678226194ead1589ea383a392f02c94904dfb873736d08d0e1f","observation_id":"44a744f7-a9b9-495b-92af-f4c0be7ea1c3","resolution":{"observed_at":"2026-08-04T00:38:15.549016Z","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":"2026-08-04T00:38:15.604188Z","title":"Advisory Circular 25.571-1D: Damage Tolerance and Fatigue Evaluation of Structure.https://www.faa.gov/documentLibrary/media/ Advisory_Circular/AC_25_571-1D_.pdf, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.604188Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:d0868625f33bf473616c1cfa1c8e2ccf56c435101d476695079180e0cbf1c672","observation_id":"a10e1733-6488-420d-95e4-702bfd2b9e91","resolution":{"observed_at":"2026-08-04T00:38:15.604188Z","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":"2026-08-04T00:38:15.707743Z","title":"Airframe digital twin technology adaptability assessment and technology demonstration.Engineering Fracture Mechan- ics, 225:106793, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.707743Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:de1f92083f42f8708319411a168b69db95ce5760a62a322554c1722db0010a9c","observation_id":"a7a596dd-642a-4906-adf8-e950fc0e2cd8","resolution":{"observed_at":"2026-08-04T00:38:15.707743Z","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":"2026-08-04T00:38:15.791310Z","title":"Probabilistic methods for risk assess- ment of airframe digital twin structures.Engineering Fracture Mechanics, 221:106674,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.791310Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:1312aa86a46cd5658a9ebebda8f41e1be41a85b23eb8f95d5a0d641b3139cf3a","observation_id":"a94ab607-a588-4a50-bb36-06b01431fe2c","resolution":{"observed_at":"2026-08-04T00:38:15.791310Z","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":"2026-08-04T00:38:15.918151Z","title":"McDowell","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.918151Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:e664c4dafb751873205f5ff1bee6dac2b08a602a28cf93255e0bfdd7815dca4d","observation_id":"c2a5a50f-9448-459d-9ad4-096fa708a860","resolution":{"observed_at":"2026-08-04T00:38:15.918151Z","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":"2026-08-04T00:38:16.024752Z","title":"Yeratapally, Patrick E","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.024752Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:84dcacc0dcb1e5f8d5c61661ddb625c94aea2084ff2712d716a5546e9ef33cf4","observation_id":"1ce5339a-d2c6-4765-a1f7-7d5bb2df021a","resolution":{"observed_at":"2026-08-04T00:38:16.024752Z","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":"2026-08-04T00:38:15.952388Z","title":"doi: https://doi.org/10.1016/j.engfracmech.2019.106673","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:15.952388Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:ca4f33236b03f75f025a4852827662b12c0cdc8f50e24f0f7381ed0c927a6582","observation_id":"6b3fb4ab-1801-4e6f-a2dc-c443a1cd0bde","resolution":{"observed_at":"2026-08-04T00:38:15.952388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.01294","last_updated":"2024-03-09T02:48:15Z","snapshot_observed_at":"2026-08-08T15:55:58.518611Z","submitted_at":"2023-04-03T18:35:28Z","title":"Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.01294","snapshot_observed_at":"2026-08-04T00:38:16.244406Z","title":"Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes, March 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.244406Z"},"links":{"cited_paper":"/paper/2304.01294","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:4b1184f28adf143f5f6597ff789ee1e1e5f9ee6e9ac9d60c952c69d0bb482f04","observation_id":"c010828a-bb47-4fdb-9301-190b127027fc","resolution":{"observed_at":"2026-08-04T00:38:16.244406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.04465","last_updated":"2024-03-19T01:38:12Z","snapshot_observed_at":"2026-08-04T19:40:17.279957Z","submitted_at":"2023-11-08T05:26:58Z","title":"Solving High Frequency and Multi-Scale PDEs with Gaussian Processes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.04465","snapshot_observed_at":"2026-08-04T00:38:16.329921Z","title":"Solv- ing High Frequency and Multi-Scale PDEs with GaussianProcesses.The Twelfth Interna- tional Conference on Learning Representations (ICLR 2024), March 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.329921Z"},"links":{"cited_paper":"/paper/2311.04465","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:3c23b104c4108efd0ec7824b9afb7675409c1e88161212ba0e1360300a2d931c","observation_id":"306919f2-3b77-47fd-bb4d-5e28d1ab6935","resolution":{"observed_at":"2026-08-04T00:38:16.329921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.00060","last_updated":"2018-03-29T20:21:30Z","snapshot_observed_at":"2026-07-06T05:36:07.769337Z","submitted_at":"2017-03-31T21:13:08Z","title":"Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.00060","snapshot_observed_at":"2026-08-04T00:38:16.519474Z","title":"Aoi, and Jonathan W","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.519474Z"},"links":{"cited_paper":"/paper/1704.00060","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:c05797568c5d193559fdac13e3b86afebc183b4edf261821e38b775ab09390b7","observation_id":"73a6d0d2-d8cb-4517-b542-de60bda47aeb","resolution":{"observed_at":"2026-08-04T00:38:16.519474Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05796","last_updated":"2020-10-08T04:36:28Z","snapshot_observed_at":"2026-08-02T19:15:45.034685Z","submitted_at":"2020-04-13T07:08:02Z","title":"Explicit Estimation of Derivatives from Data and Differential Equations by Gaussian Process Regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05796","snapshot_observed_at":"2026-08-04T00:38:16.590763Z","title":"Explicit Estimation of Derivatives from Data and Dif- ferential Equations by Gaussian Process Regression, October 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.590763Z"},"links":{"cited_paper":"/paper/2004.05796","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:60467e05e440676fdd48fb6d025ea116b915b1c31bba2d8223d782cd867e3dd2","observation_id":"63fc4eda-0139-483c-8b34-34fea08baed5","resolution":{"observed_at":"2026-08-04T00:38:16.590763Z","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":"2026-08-04T00:38:16.422894Z","title":"Derivative Observations in Gaussian Process Models of Dynamic Systems","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.422894Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:db9cdb39b37a49afe49c3e7b3394c6c93956b501d9a051b28f5aa7047c600d0c","observation_id":"afe6527b-6c29-42d4-9316-40d95c22320b","resolution":{"observed_at":"2026-08-04T00:38:16.422894Z","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":"2026-08-04T00:38:16.757512Z","title":"Scaling Gaussian Processes with Derivative Information Using Variational Inference","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.757512Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:aaa9210f1e50fddc028d9e695c4e338e9c2312f1ca3177b4a2177332a275c8ea","observation_id":"4568f19f-e014-45df-bfbd-bf5759455033","resolution":{"observed_at":"2026-08-04T00:38:16.757512Z","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":"2026-08-04T00:38:16.847901Z","title":"DGP-LVM: Derivative Gaussian process latent variable models.Statistics and Computing, 35(5):120, October","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.847901Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:bbeaf270734f1785f7ecd3d50664fe3c2f6a7a014db6094fea723ec62fc4e4d5","observation_id":"6fb8a659-d210-4752-989e-1c9f86047fc0","resolution":{"observed_at":"2026-08-04T00:38:16.847901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.12283","last_updated":"2018-10-29T17:51:54Z","snapshot_observed_at":"2026-07-06T07:11:16.512397Z","submitted_at":"2018-10-29T17:51:54Z","title":"Scaling Gaussian Process Regression with Derivatives","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.12283","snapshot_observed_at":"2026-08-04T00:38:16.658416Z","title":"Scaling Gaussian Process Regression with Derivatives, October 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.658416Z"},"links":{"cited_paper":"/paper/1810.12283","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:d31b1b8aa5271e6ff07d6ee793a4b3119b9e0ec9c8726d058fdc0545c3f04b77","observation_id":"9266dca5-1f73-422e-8bb9-16ca047bf1bd","resolution":{"observed_at":"2026-08-04T00:38:16.658416Z","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":"2026-08-04T00:38:17.052415Z","title":"Hoerl and Robert W","venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.052415Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:3c620328a211606870d1d0ae46631e2e4a58eceaa8ea8843701c3f7186dc543f","observation_id":"d268015a-9158-4d2a-8a74-3082a77f57bd","resolution":{"observed_at":"2026-08-04T00:38:17.052415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"0901.2024","last_updated":"2009-07-06T14:35:33Z","snapshot_observed_at":"2026-07-06T01:48:48.825696Z","submitted_at":"2009-01-14T14:38:08Z","title":"Critical current of a Josephson junction containing a conical magnet","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"0901.2024","snapshot_observed_at":"2026-08-04T00:38:17.118450Z","title":"Success rate of evolution strategies on the mul- timodal griewank function","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.118450Z"},"links":{"cited_paper":"/paper/0901.2024","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:7d175d56c57f0cf7fc48775f6385019f58b530625d119e5e2a3cff0fda49cb81","observation_id":"9197014c-1df2-4882-b9a0-189bd2bcac75","resolution":{"observed_at":"2026-08-04T00:38:17.118450Z","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":"2026-08-04T00:38:17.198784Z","title":"When Gaussian Process Meets Big Data: A Review of Scalable GPs.IEEE Transactions on Neural Networks and Learning Systems, 31(11):4405–4423, November 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.198784Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:1ed849a9d44e612a017c80a7795068f5924fb1d09c13cccdc3a6c102564bd8d8","observation_id":"1241e163-b2c1-463c-9f64-11f2f6702fa8","resolution":{"observed_at":"2026-08-04T00:38:17.198784Z","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":"2026-08-04T00:38:16.973352Z","title":"Hida-matérn kernel hida-matérn kernel","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.973352Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:eb74a58d73c622f47028c65d58a599e7fe0bcf88c48efe979510516c5aa9ffb4","observation_id":"7aeab75e-bf98-458f-8144-6dd7a2be3bc5","resolution":{"observed_at":"2026-08-04T00:38:16.973352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07583","last_updated":"2017-11-03T14:40:15Z","snapshot_observed_at":"2026-07-06T04:57:27.652360Z","submitted_at":"2016-05-24T18:56:57Z","title":"Recursive Sampling for the Nystr\\\"om Method","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07583","snapshot_observed_at":"2026-08-04T00:38:17.332544Z","title":"Recursive Sampling for the Nyström Method, November 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.332544Z"},"links":{"cited_paper":"/paper/1605.07583","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:eb48d10f7c31d79a1a725a4d465a59d413ad0b8055f1bd401c5ce47e25385c42","observation_id":"40c04ae3-76fa-460a-bf48-3560aff195bc","resolution":{"observed_at":"2026-08-04T00:38:17.332544Z","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":"2026-08-04T00:38:17.396561Z","title":"Epperly, Joel A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.396561Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:127a1200d7b69a7ed86f5f30e93743fbe9092738ccd75c449fe349bfb6042732","observation_id":"8233fa04-58ae-4031-8f1a-9fa093306319","resolution":{"observed_at":"2026-08-04T00:38:17.396561Z","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":"2026-08-04T00:38:17.464543Z","title":null,"venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.464543Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:303c777f745500df19cf40e2098a618ad66e8b9398c391b688368cec8328d181","observation_id":"a2c9e6f4-b7b8-4413-8bbc-d05e405d785e","resolution":{"observed_at":"2026-08-04T00:38:17.464543Z","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":"2026-08-04T00:38:17.263458Z","title":"Heaton, Abhirup Datta, Andrew O","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.263458Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:dd130ae41152bf981a680d0aadde482e7189ed481ca386739f847beebd0e0554","observation_id":"bf1c30d8-33ea-4741-9ee1-b0f675d54019","resolution":{"observed_at":"2026-08-04T00:38:17.263458Z","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":"2026-08-04T00:38:17.628248Z","title":"Finley, and Alan E","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.628248Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:d5d9965038677e6bab23305986c18eaf67f790d3cf8183147122acfbc1647a40","observation_id":"ed34c389-8d98-454c-906b-b45fca9a840e","resolution":{"observed_at":"2026-08-04T00:38:17.628248Z","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":"2026-08-04T00:38:17.710872Z","title":"Finley, Nicholas A","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.710872Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:e3b7dc9c66b0dfd1287f2fe4fa48074d8ef2b786b8121256c9b2ae2236db5cbe","observation_id":"b35121c6-0dfb-4795-a7d1-fd69db2d08ed","resolution":{"observed_at":"2026-08-04T00:38:17.710872Z","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":"2026-08-04T00:38:17.754214Z","title":"Sparse greedy gaussian process regression","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.754214Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:623f98740b735491ea2d2e397dd353492f920011a77faf40418b95acae34100f","observation_id":"03a87b90-6597-417a-af08-2ae2979a7c53","resolution":{"observed_at":"2026-08-04T00:38:17.754214Z","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":"2026-08-04T00:38:17.570609Z","title":"Covariance Tapering for Interpolation of Large Spatial Datasets.Journal of Computational and Graphical Statistics, 15(3):502–523, September 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.570609Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:ef021e3ef8c7e76439eeb2d6ef11e84179a19c54eebaa29859934dd43de5911b","observation_id":"b829075a-81e1-443e-a25b-75e11e900001","resolution":{"observed_at":"2026-08-04T00:38:17.570609Z","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":"2026-08-04T00:38:17.910321Z","title":"Random Features for Large-Scale Kernel Machines","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.910321Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:6c86ee55c4c58ea8ed8c8f1aeada93a764a45b3fec44159c496d6538e72a6b7f","observation_id":"1a7f99b5-7ade-41d8-a136-3b640fae83d5","resolution":{"observed_at":"2026-08-04T00:38:17.910321Z","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":"2026-08-04T00:38:18.018596Z","title":"A unifying view of sparse ap- proximate gaussian process regression.Journal of Machine Learning Research, 6(65): 1939–1959, 2005","venue":null,"work_id":null,"year":1939},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.018596Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:a7f8f784e7641979e4e74ed00028e467a530dc3dd73f5e31c785d6a4b72ce25a","observation_id":"65a5b49f-b097-4900-8a6e-9f22cc7afe36","resolution":{"observed_at":"2026-08-04T00:38:18.018596Z","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":"2026-08-04T00:38:18.071249Z","title":"Stein, Zhiyi Chi, and Leah J","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.071249Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:4926ed82bbc399eedda9dde12a47be0abb48b2a4aea7e058a43a5acb5b89b3b0","observation_id":"484f9a9d-e865-4944-8034-f424d7463309","resolution":{"observed_at":"2026-08-04T00:38:18.071249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13303","last_updated":"2023-05-26T19:48:13Z","snapshot_observed_at":"2026-07-06T14:46:23.137171Z","submitted_at":"2023-01-30T21:50:08Z","title":"Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.13303","snapshot_observed_at":"2026-08-04T00:38:17.807913Z","title":"Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization, May 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:17.807913Z"},"links":{"cited_paper":"/paper/2301.13303","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:0cb4f17459d2902fa66c0036dee08e3803a9d7bdd250164d213482824c245cca","observation_id":"06976181-6d32-4d48-be38-fc29f867d673","resolution":{"observed_at":"2026-08-04T00:38:17.807913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00386","last_updated":"2021-07-20T15:43:56Z","snapshot_observed_at":"2026-07-06T09:16:51.250860Z","submitted_at":"2020-05-01T14:08:31Z","title":"Scaled Vecchia approximation for fast computer-model emulation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00386","snapshot_observed_at":"2026-08-04T00:38:18.194970Z","title":"Scaled Vecchia approximation for fast computer-model emulation, July 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.194970Z"},"links":{"cited_paper":"/paper/2005.00386","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:bf481c27dd4baab2a247e741ffff50b7d243a0bbc14d2c6e059fa16a7c043227","observation_id":"c42692a7-a9ef-40ce-9c48-a1e8c5efcf8d","resolution":{"observed_at":"2026-08-04T00:38:18.194970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.11648","last_updated":"2025-05-09T02:21:08Z","snapshot_observed_at":"2026-07-06T15:57:01.182708Z","submitted_at":"2023-07-21T15:27:17Z","title":"Sparse inverse Cholesky factorization of dense kernel matrices by greedy conditional selection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.11648","snapshot_observed_at":"2026-08-04T00:38:18.267569Z","title":"Sparse inverse Cholesky factorization of dense kernel matrices by greedy conditional selec- tion, May 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.267569Z"},"links":{"cited_paper":"/paper/2307.11648","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:4c0ac83bde004e9f280662410fe4b08f429f8641778d44126f7b34312c264d7d","observation_id":"e52ee6db-2cea-4b75-ac27-67b26335d4be","resolution":{"observed_at":"2026-08-04T00:38:18.267569Z","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":"2026-08-04T00:38:18.358213Z","title":"Permutation and Grouping Methods for Sharpening Gaussian Process Approximations.Technometrics, 60(4):415–429, October 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.358213Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:346822ec19593cac9631fdccd0dea9ebe4f8bd14ac5b9b0c658a77b33b7be355","observation_id":"c1336e33-aca4-47c2-98f9-9440e5d10b90","resolution":{"observed_at":"2026-08-04T00:38:18.358213Z","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":"2026-08-04T00:38:18.136503Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.136503Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:b07d20d00610bb966748d4421478861f2a2629600ba1b67833200edcf3159e86","observation_id":"660886fd-5606-478f-878d-9de68552e1c3","resolution":{"observed_at":"2026-08-04T00:38:18.136503Z","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":"2026-08-04T00:38:18.546172Z","title":"A General Framework for Vec- chia Approximations of Gaussian Processes.Statistical Science, 36(1), Febru- ary 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.546172Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:6835ec1276bfba1cc055ad8fe6c5b47d1f712d2bc63a6f029748c40dedb20bb4","observation_id":"6ae3d2d5-a0a7-4a4e-b1b7-8a70764c5d60","resolution":{"observed_at":"2026-08-04T00:38:18.546172Z","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":"2026-08-04T00:38:18.650654Z","title":"Sparse Recovery of Elliptic Solvers from Matrix- Vector Products.SIAM Journal on Scientific Computing, 46(2):A998–A1025, April 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.650654Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:38dc21b5a493c6a2943cf09a72a464405bced3ddcf00609631120b0daa19fe00","observation_id":"b60b18b3-1c29-4890-9e83-60ba5e16f96a","resolution":{"observed_at":"2026-08-04T00:38:18.650654Z","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":"2026-08-04T00:38:18.718594Z","title":"Sparse Cholesky Factorization by Kullback–Leibler Minimization.SIAM Journal on Scientific Computing, 43(3):A2019– A2046, January 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.718594Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:5a2945432ab506f0749133d91cd7354899bf2822083732451ed992c88066a1b8","observation_id":"c597929a-24dc-4ae6-b782-9ced8c4a2afb","resolution":{"observed_at":"2026-08-04T00:38:18.718594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.12959","last_updated":"2021-08-10T22:50:52Z","snapshot_observed_at":"2026-07-06T10:52:48.792727Z","submitted_at":"2021-03-24T03:16:08Z","title":"Solving and Learning Nonlinear PDEs with Gaussian Processes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.12959","snapshot_observed_at":"2026-08-04T00:38:18.778014Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.778014Z"},"links":{"cited_paper":"/paper/2103.12959","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:de67ab927964b0c84080c931ace04a7b8d2158c10e8662be782c1919bae09f53","observation_id":"2422990e-b674-4208-b2f3-2c7f719da540","resolution":{"observed_at":"2026-08-04T00:38:18.778014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.14591","last_updated":"2023-04-07T20:01:33Z","snapshot_observed_at":"2026-07-06T12:23:15.205001Z","submitted_at":"2021-12-29T15:04:16Z","title":"Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.14591","snapshot_observed_at":"2026-08-04T00:38:18.482279Z","title":"Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference.Statistics and Computing, 33(3):56, June 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.482279Z"},"links":{"cited_paper":"/paper/2112.14591","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:d34d3ec4096b5c301fa1bc233b0c53850fa3f95940f73a5231c689cc4151b449","observation_id":"d1b90ec7-63f9-434d-8757-7992ac54e471","resolution":{"observed_at":"2026-08-04T00:38:18.482279Z","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":"2026-08-04T00:38:18.926075Z","title":"Davis and William W","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.926075Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:01d0c829967823502f5b623ec9603654c41ae96a9f8a0eba9624e77cd3e7a04f","observation_id":"d9daadf9-b559-4331-ac3a-78fb61504ac4","resolution":{"observed_at":"2026-08-04T00:38:18.926075Z","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":"2026-08-04T00:38:18.996807Z","title":"Leser, James E","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.996807Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:06be63f398d36771d64ee7cffd9203d4907d42a380db1e9d73735e0513b90b94","observation_id":"41d01532-f65a-4ad4-9a8e-4f080356d9da","resolution":{"observed_at":"2026-08-04T00:38:18.996807Z","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":"2026-08-04T00:38:19.057626Z","title":"Computational fracture mechanics.Encyclopedia of computational mechanics, 2004","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:19.057626Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:ffd310adb3330d1edb6b60b886a48d528d75f2bd6cdfb86fbe4884f52e42283a","observation_id":"cc056af1-c91c-49f9-a3d5-e03b8fc078f4","resolution":{"observed_at":"2026-08-04T00:38:19.057626Z","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":"2026-08-04T00:38:19.169336Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:19.169336Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:6f6416f697c1a0ef7775830644c5965f8cbea9d14dee06a0ea4877b08d736f8e","observation_id":"4c658907-9d8c-499f-a15b-103f1ab82284","resolution":{"observed_at":"2026-08-04T00:38:19.169336Z","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":"2026-08-04T00:38:18.838258Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.838258Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:7dc6793f02dd6ea2c697ccd7a7e58fe4068054f09e714fd71f06a4ea9b9c6ec5","observation_id":"e48bfd2d-ed9c-411e-9ca2-5f3af56a4426","resolution":{"observed_at":"2026-08-04T00:38:18.838258Z","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":"2026-08-04T00:38:19.300039Z","title":"point-wise ordering algorithm 2","venue":null,"work_id":null,"year":1969},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:19.300039Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:e61ef41f50cfc3b3cff9a38704462e48ad21d2f6b1a827d20df771223c06bdc0","observation_id":"0a5f3e5c-42ef-42fc-a175-87fa60e2940a","resolution":{"observed_at":"2026-08-04T00:38:19.300039Z","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":"2026-08-04T00:38:19.207194Z","title":"Kirby, and Jacob Hochhalter","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:19.207194Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:ad8cf34ac0a443c86d95ce15e26987b6fa7d7b9672f35f5936f83dfac6fbe90c","observation_id":"b7ed2e52-3636-46b9-98d5-ff730219adbb","resolution":{"observed_at":"2026-08-04T00:38:19.207194Z","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":"2026-08-04T00:38:19.391460Z","title":"point-wise ordering algorithm 2","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:19.391460Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:a22d9e30dab12b2e6b4909a2e063cda804bdfd66e1fa0dbce695b8ce109f73c8","observation_id":"2c95e0a7-f693-485d-a4b2-6180c60a02a3","resolution":{"observed_at":"2026-08-04T00:38:19.391460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04074","last_updated":"2025-01-31T16:01:37Z","snapshot_observed_at":"2026-08-07T12:48:41.765177Z","submitted_at":"2024-04-05T13:03:13Z","title":"DGP-LVM: Derivative Gaussian process latent variable models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04074","snapshot_observed_at":"2026-08-04T00:38:16.917196Z","title":"doi: 10.1007/s11222-025-10644-4","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.917196Z"},"links":{"cited_paper":"/paper/2404.04074","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:08917473dfab432088df04d68392c26f2f1e350f9cbd37c69f04199a11a7de6d","observation_id":"11eb4e8f-0653-47f8-825f-1c7d5fb4d18a","resolution":{"observed_at":"2026-08-04T00:38:16.917196Z","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":"2026-08-04T00:38:18.438178Z","title":"URLhttp://arxiv.org/abs/1609","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":2723,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:18.438178Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:8fd33e1ba7b8510b342b846a571421837b63147daaa8e834ff4234439f03b48d","observation_id":"f77bbd50-6a04-463d-b8c3-72dcfb5d8934","resolution":{"observed_at":"2026-08-04T00:38:18.438178Z","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":"2026-08-04T00:38:16.101014Z","title":"URLhttps://www","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":7944,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.101014Z"},"links":{"citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:53ee7729401763e745436389617b21fca1133fb624618b68794991cbb39f8e6c","observation_id":"dfb89a30-13ec-41ab-a760-61a6e105d7ce","resolution":{"observed_at":"2026-08-04T00:38:16.101014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":6,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":50,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":56},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2511.00366."}