{"as_of":"2026-08-17T23:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dbf400f50bedef04d4e720d02eef5bda053e6471b62a64f47b4335ba89190998","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:12:25.513677Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2411.17433/citation-record","integrity":"/paper/2411.17433/integrity","json":"/paper/2411.17433/citation-record.json","paper":"/paper/2411.17433"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:12:26.482175Z","title":"Mortensen, H","venue":null,"work_id":"ba170247-8295-4c0a-b33f-b953efbe159c","year":2016},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.223014Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:2faa01efa88ee07db5782d18ec20657f2f96d8c6636878b772ea18d061203724","observation_id":"6bb94be8-b00c-4a14-8170-a3868e8cc64a","resolution":{"observed_at":"2026-08-12T12:12:26.486304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.469044Z","title":null,"venue":null,"work_id":"890b4329-f74c-43dc-863a-31232d0639af","year":2020},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.227710Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:f5741aeb4fbeb3957427021370a01f6a6e5b6f0377ab7ad141bbdaa185538c8b","observation_id":"29fe2bee-f9e1-42cf-a019-5d5644201e6d","resolution":{"observed_at":"2026-08-12T12:12:26.473304Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.454986Z","title":"Abad ´ ıa-Heredia, M","venue":null,"work_id":"d87a7f88-015c-44eb-b469-6894c4032f3a","year":2022},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.231818Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:fc35254bb9ddc8b49571b1d28173e510d5bbcb15e4c4aeb68c5d4068a6ce7abc","observation_id":"d9f54026-202a-4003-af02-dfdc580a6283","resolution":{"observed_at":"2026-08-12T12:12:26.459092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.09860","last_updated":"2023-01-24T08:39:20Z","snapshot_observed_at":"2026-08-17T02:36:00.374689Z","submitted_at":"2023-01-24T08:39:20Z","title":"A predictive physics-aware hybrid reduced order model for reacting flows","version":1},"cited_work":{"arxiv_id":"2301.09860","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.09860","snapshot_observed_at":"2026-08-12T12:12:25.649484Z","title":"A predictive physics-aware hybrid reduced order model for reacting flows","venue":"cs.LG","work_id":"7d45b1df-bf82-47f4-a98d-f6cd905704f1","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.236121Z"},"links":{"cited_paper":"/paper/2301.09860","citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:7b76f1f5f6f84ca7d0666d5dd626d12c49f5b2b9dc2056c3130ae4cfa9698aba","observation_id":"1a5818d5-4f36-4e89-a8d9-1907b223adfc","resolution":{"observed_at":"2026-08-12T12:12:25.654136Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08832","last_updated":"2023-05-15T17:49:02Z","snapshot_observed_at":"2026-08-17T21:35:45.760351Z","submitted_at":"2023-05-15T17:49:02Z","title":"Deep Learning combined with singular value decomposition to reconstruct databases in fluid dynamics","version":1},"cited_work":{"arxiv_id":"2305.08832","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.08832","snapshot_observed_at":"2026-08-12T12:12:25.629928Z","title":"Deep Learning combined with singular value decomposition to reconstruct databases in fluid dynamics","venue":"physics.flu-dyn","work_id":"f2cb2b48-4741-42a4-b2f8-21fe3d56a4b0","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.241264Z"},"links":{"cited_paper":"/paper/2305.08832","citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:ca71fb8bf3a52c68cc369886bb3340ad169f19750bcce6d4ef741af0e34f39a3","observation_id":"17f326f3-7ecb-4ed6-a50c-5d9ac8a98609","resolution":{"observed_at":"2026-08-12T12:12:25.634526Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.440559Z","title":"Parente, J","venue":null,"work_id":"7c9652c8-af0e-431a-a96e-8ed07268e2be","year":2013},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.246431Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:42e5db25f1f1213b7cf23417925ff3831145c09cbc8726594b082c52e9d2a746","observation_id":"d3d87281-2524-4f99-923b-4d0ad062b6a4","resolution":{"observed_at":"2026-08-12T12:12:26.444901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.426663Z","title":"Scherl, B","venue":null,"work_id":"dcfdc5ba-9c9d-4364-83f9-72e7fa3713b3","year":2020},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.251884Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:338019ebe96190548e0580de42d21063861871fecca2dc59365713265040301c","observation_id":"796817c0-45fe-4fc7-8308-73ed1a1fd94c","resolution":{"observed_at":"2026-08-12T12:12:26.431128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.412069Z","title":"Le Clainche, J","venue":null,"work_id":"38831f58-fe1b-4ae7-81ef-3c8ffad67406","year":2017},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.256125Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:b87b03856f25d9abfd5e5a91e92928fdc9caebcc43a396eb9ebbda848eab7138","observation_id":"86671bd0-8a7f-4bed-9acf-22b25eb96455","resolution":{"observed_at":"2026-08-12T12:12:26.417540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.398943Z","title":"Le Clainche, R","venue":null,"work_id":"f814b9ab-4e9b-4c7e-ae54-7382b8206f03","year":2019},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.260357Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:9abec9e38ed0fc2114faf6452c99f4d910caf486abbe9c9b89a3264481409bbc","observation_id":"65dc042e-6464-4669-bd9e-860ef37d5498","resolution":{"observed_at":"2026-08-12T12:12:26.403188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.385234Z","title":"Corrochano, G","venue":null,"work_id":"8e2ec6b6-2337-4248-b57b-01081a538358","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.264326Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:c1253e3875cc8817bdec18fae808948fca040b08fbe550c20fbdca3889764f9f","observation_id":"74854c2f-cf7c-4901-b869-41c23fc45178","resolution":{"observed_at":"2026-08-12T12:12:26.389985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.12731","last_updated":"2023-06-12T07:55:18Z","snapshot_observed_at":"2026-08-16T16:06:11.791242Z","submitted_at":"2022-12-24T12:59:41Z","title":"Forecasting through deep learning and modal decomposition in two-phase concentric jets","version":3},"cited_work":{"arxiv_id":"2212.12731","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.12731","snapshot_observed_at":"2026-08-12T12:12:25.611460Z","title":"Forecasting through deep learning and modal decomposition in two-phase concentric jets","venue":"cs.LG","work_id":"fd7579a9-680c-4b8b-86a8-ecf35fd4a191","year":2022},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.268393Z"},"links":{"cited_paper":"/paper/2212.12731","citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:2d3b1436812ed05151fc5cf51bb58857ce048816034ecaa270a11662fc69f713","observation_id":"0ce5d232-3946-4451-b883-3bb4c15cf59a","resolution":{"observed_at":"2026-08-12T12:12:25.616283Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.371234Z","title":"Huang, T","venue":null,"work_id":"d23fef6f-a35b-4612-bbfe-2d7e336f7b07","year":2022},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.272737Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:4fda4d9022153092dac5957a7a6249de2547a40cf33dd9ba39d9d581d5e92482","observation_id":"b745da89-6ad2-4263-a711-c062a91a3d11","resolution":{"observed_at":"2026-08-12T12:12:26.375585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.358122Z","title":"Mu˜ noz, H","venue":null,"work_id":"864f6502-98e8-4a7f-9f10-a83cca859aa6","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.276970Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:9a3df702bf2c3a11ceff8e55d0f4b6bfaa307c13a535e5d03a71827baa4caf5a","observation_id":"a7ea72a2-b349-491d-b4f6-ed3a32741eed","resolution":{"observed_at":"2026-08-12T12:12:26.362386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.344445Z","title":"Eivazi, S","venue":null,"work_id":"484f91e4-2d51-40da-b256-ceaf6fbff52a","year":2022},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.280943Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:a323efd44d3eb48958fe3d5c398b47dc484e7626ecafeaa3e3086854604ef40b","observation_id":"c1b5cef7-e435-4fdd-8f47-f3f11b350038","resolution":{"observed_at":"2026-08-12T12:12:26.349151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.330510Z","title":"Soto-Valle, S","venue":null,"work_id":"13446272-f759-4954-8bf6-0ac8bb1d707f","year":2020},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.284859Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:35cdd6abb6ea1db44e0c2d5ad4223a86561b1de3678b441af411e8e69816557e","observation_id":"ef4d60a7-e106-44c6-b044-43d7d414fcba","resolution":{"observed_at":"2026-08-12T12:12:26.335215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.318148Z","title":null,"venue":null,"work_id":"91f9eef3-c99b-4aa4-826c-f328f7c2ecb3","year":2018},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.288862Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:a8f78c6fc245411e9a3e5d5ac7f97501f0399f579b0b5d07fb3bde7d29546a51","observation_id":"dd76562b-a016-41b8-8880-e64fa7ff0dba","resolution":{"observed_at":"2026-08-12T12:12:26.321966Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.305437Z","title":"Woodward, Y","venue":null,"work_id":"6da9a8cf-5ae7-443f-9920-c09a1a225f8a","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.292806Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:418d6718b45df935795129c0cd5039753dd3fd238c9118605a54947909576062","observation_id":"5f2a6171-3b0f-47d6-bd7c-dc9fbd5db7a3","resolution":{"observed_at":"2026-08-12T12:12:26.309618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.293487Z","title":null,"venue":null,"work_id":"f7aeb274-fd29-4571-ab03-3fb9bf8c679e","year":2018},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.296882Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:02c074f1fc22225c6806ebf3fb4d5a07886c875d6b75c2166e05d426c67d296d","observation_id":"24332378-7db3-4c04-9a46-3f26eb5b85cd","resolution":{"observed_at":"2026-08-12T12:12:26.297322Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.281609Z","title":"Siano, E","venue":null,"work_id":"c96e7ed9-3456-4d78-a40f-122e5e3921b7","year":2017},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.301295Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:a388dca25ab2878ea29c8baa915f8fbca8c8ccfd4c852e92b3d1d629921648dc","observation_id":"4896d476-e382-4127-8748-b481fb4774fc","resolution":{"observed_at":"2026-08-12T12:12:26.285640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17150","last_updated":"2023-05-26T08:29:12Z","snapshot_observed_at":"2026-08-16T15:29:25.294794Z","submitted_at":"2023-05-26T08:29:12Z","title":"ModelFLOWs-app: data-driven post-processing and reduced order modelling tools","version":1},"cited_work":{"arxiv_id":"2305.17150","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.17150","snapshot_observed_at":"2026-08-12T12:12:25.592534Z","title":"ModelFLOWs-app: data-driven post-processing and reduced order modelling tools","venue":"cs.CE","work_id":"53938024-fe91-42a2-8f45-bab10b310a42","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.306114Z"},"links":{"cited_paper":"/paper/2305.17150","citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:442e2d8cfb1847825c3eec6bcf793ef4b3f3070982f93a61e4f552bdabaf4fd5","observation_id":"996ecd2c-a539-4123-b44a-fec6be0b57e3","resolution":{"observed_at":"2026-08-12T12:12:25.597599Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.269756Z","title":null,"venue":null,"work_id":"9a8e11b4-3a56-4660-b1e2-ee0e2f971426","year":2015},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.310942Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:5850e2d5696f57601fb2238468d3ad498a1d4b9acea22bb8276edf822a5992d0","observation_id":"099b7e3c-40a5-4693-b296-c1ae9d167497","resolution":{"observed_at":"2026-08-12T12:12:26.273415Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.07358","last_updated":"2020-12-28T01:54:44Z","snapshot_observed_at":"2026-08-14T17:14:04.877910Z","submitted_at":"2019-02-20T00:29:35Z","title":"Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors","version":2},"cited_work":{"arxiv_id":"1902.07358","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.07358","snapshot_observed_at":"2026-08-12T12:12:25.571523Z","title":"Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors","venue":"physics.comp-ph","work_id":"a5370af3-c44f-461d-addd-b4858658ffe5","year":2019},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.314780Z"},"links":{"cited_paper":"/paper/1902.07358","citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:ae868ac8ccabc2aeb542bc63ffb935addade889b75e914a62e43d1c690cd4b0b","observation_id":"5cfadbbb-ba60-4313-8aed-3ae57d1f3ed2","resolution":{"observed_at":"2026-08-12T12:12:25.577939Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.255515Z","title":"de Silva, K","venue":null,"work_id":"5b2a570d-becd-4e99-9d90-994dc3a23b8c","year":2021},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.319205Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:ddd2e3cb6dfd07573e7788ec226b45d06ab6f189a61a965de7a9b6cd1d385822","observation_id":"742594b5-4343-48d9-b5ff-4dd4a87c6643","resolution":{"observed_at":"2026-08-12T12:12:26.259813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.242238Z","title":"Arciniega-Ceballos, M","venue":null,"work_id":"84a3aead-0caa-4d90-a20f-66c6c605d93c","year":2012},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.323279Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:db294ce5d7a17228983d18a9a19fb8e0d5790e0ae2c76d87b1c62900afe6dcd5","observation_id":"5f3ad1cb-5094-4a7a-b728-cd827903b73c","resolution":{"observed_at":"2026-08-12T12:12:26.246594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.228996Z","title":null,"venue":null,"work_id":"c3f1dfba-6688-48bf-ae98-8c0ee8e5103e","year":2016},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.327506Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:4057a23f0e080ff4590b26eb06317b732bac5d963466b04ad03f8e338649e81c","observation_id":"cdb7bcac-496f-4f13-9b6e-e6d2365b417d","resolution":{"observed_at":"2026-08-12T12:12:26.233423Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.216028Z","title":"Umargono, J","venue":null,"work_id":"b6295bf4-28ef-42cb-92eb-bfa4e99aff64","year":2019},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.331643Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:58d756fcfec90111838a96549039c862333e2493eeeffc88445f4be579291f2f","observation_id":"c5032b4b-61e4-427b-af66-e5977c329392","resolution":{"observed_at":"2026-08-12T12:12:26.220339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.201539Z","title":"Sirovich, Turbulence and the dynamic of coherent structures, parts i–iii, Q","venue":null,"work_id":"6acfccd0-ddc3-4c1b-9a9c-60938855fedf","year":1987},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.335478Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:5e58afd8f9d5a124cce370a7609fe716ec7ef236c497cdada3c063f9e162206e","observation_id":"d19fc4b0-85fc-4fd4-981d-7beabce3f1da","resolution":{"observed_at":"2026-08-12T12:12:26.206170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.188305Z","title":null,"venue":null,"work_id":"9b125be9-c8b1-4127-a3e0-df0ffadaf433","year":1967},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.339781Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:cc50d46056c3202ccf525bdf255b498dd0901beeec7a868772c59c46993cf5e3","observation_id":"281327ea-d4f8-4321-ad44-acb36b3e5049","resolution":{"observed_at":"2026-08-12T12:12:26.192576Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.344078Z","title":"Le Clainche, D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.344078Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:d963a2b6c849685a32851ce60bb3a76e5b618b5b05d6439c3298ad4250549226","observation_id":"8c80ddec-bda3-40db-956e-8123f1c9365d","resolution":{"observed_at":"2026-08-12T12:12:25.344078Z","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":"2026-08-12T12:12:26.175003Z","title":"Parente, J","venue":null,"work_id":"963ed87a-37b8-43d5-864d-122590cff765","year":2013},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.348200Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:8c9f33234e84f849b12de7271c756172a25bf49d1c8e9e5d1dbc424a917ca0e6","observation_id":"da6fee25-a231-4690-90d8-ba06923c0bd7","resolution":{"observed_at":"2026-08-12T12:12:26.179583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.161599Z","title":"Rap´ un, F","venue":null,"work_id":"6cea4586-fb43-498b-9cb6-670843b55c61","year":2017},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.352540Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:0103ecb6ed3afa615f8639a6095350d4fe156833c3756a77a0cd4dd0a50b38c6","observation_id":"06f4c816-0010-4949-a099-885b0ea4f81c","resolution":{"observed_at":"2026-08-12T12:12:26.165837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.148950Z","title":"Manohar, B","venue":null,"work_id":"3f8a2c7b-b286-45b3-9cbf-ce80a07bd4d0","year":2018},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.356985Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:52056221e42f3ad51a49b01f65633bf3644436cf2efa5006e5c807950b4337c7","observation_id":"189b78c9-8baa-48f9-bfef-7fc0858b1766","resolution":{"observed_at":"2026-08-12T12:12:26.153080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.135846Z","title":"Businger, G","venue":null,"work_id":"4d05c0d0-9fc7-4bb7-9f81-fdf47b0d1af9","year":1965},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.361021Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:7190787e2dac5687bf592961eda5ff493598bbf7ca37f0708df0b60475b025de","observation_id":"ef1ba9a3-f4cd-46c2-a6bb-3b85ffc9fd8c","resolution":{"observed_at":"2026-08-12T12:12:26.140045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.122042Z","title":"Sommariva, M","venue":null,"work_id":"075bcdcd-b46f-4051-bd24-7093db8a1936","year":2009},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.365472Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:1d9109bb461fcf29b3fc5a6d8b8627bfd3bbabeeb078fc9fffe2b1177b47da17","observation_id":"c909b4a4-1059-41a3-b40b-3d42ac4d5029","resolution":{"observed_at":"2026-08-12T12:12:26.126531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.107747Z","title":null,"venue":null,"work_id":"a39ae161-b324-4fe5-a9c9-2375e72dc8f6","year":1998},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.369595Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:2a8b0273ac5f583c2a1fcf68bf543bc81fc5a13a197bc57d1983f812c3de950f","observation_id":"d7c943c7-f64e-4618-a3c6-473171eaddc6","resolution":{"observed_at":"2026-08-12T12:12:26.112461Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.094307Z","title":"Seshadri, A","venue":null,"work_id":"06b708bf-0c9b-4ac4-a989-53a5c26a1de3","year":2017},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.373546Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:975087c4a97d4992d2d34598489b122bb865ce60cf93f8bfb53c18c19bfd7395","observation_id":"0fbc0de5-5707-4d09-8b39-beed6e797a0d","resolution":{"observed_at":"2026-08-12T12:12:26.098662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.081142Z","title":"Kuraria, N","venue":null,"work_id":"d9dfdab0-2b9a-4160-90c0-35e0e13994a4","year":2018},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.377795Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:328ed1d4e865590b897af452e9617665afd5a6a6c4e56ccaeb5cf29f6cb2ed8b","observation_id":"295bdcfd-f0f2-4acc-bc52-af07f5f22c27","resolution":{"observed_at":"2026-08-12T12:12:26.085377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.067503Z","title":null,"venue":null,"work_id":"07a0a33b-a2dc-4038-8c9f-83d8c94053fa","year":2020},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.381736Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:d797a5fd33a39ff71d3742c6f174fd2f0e717a04d179e71a9a8665e557db8d30","observation_id":"0373237d-9c46-461c-8abe-e2354019ccb4","resolution":{"observed_at":"2026-08-12T12:12:26.071884Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.053448Z","title":"Jackson, A finite-element study of the onset of vortex shedding in flow past variously shaped bodies, Journal of fluid Mechanics 182 (1987) 23–45","venue":null,"work_id":"9274c8e0-db83-476f-b0ea-4eb08250fbd0","year":1987},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.385697Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:22083187e10c9f91805fcb17fcc7cc47e4bb840723182cf8b6038bec456cfb45","observation_id":"62dee0b9-c52e-488b-894c-caafa1ab0b7f","resolution":{"observed_at":"2026-08-12T12:12:26.058381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.040164Z","title":"Barkley, R","venue":null,"work_id":"ed227e2f-dba6-40d0-b188-98ded7363a12","year":1996},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.389712Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:7852ed9e635c805de8be400adb294622d9227e541810e751a2f2864afcdbe3ae","observation_id":"c7cea966-a39a-400a-9c9b-a94b0126e93a","resolution":{"observed_at":"2026-08-12T12:12:26.044522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.026683Z","title":null,"venue":null,"work_id":"7b3af52d-b648-48ac-8bc6-ece42e322894","year":null},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.394501Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:57e445f922b077665d78590ecafd460f30483b302b11ac95869de4d48b7943e4","observation_id":"ab6223ca-ba69-43d6-a794-d96c6d96076e","resolution":{"observed_at":"2026-08-12T12:12:26.031070Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.013831Z","title":"Towne, S","venue":null,"work_id":"9705132e-4039-4f18-b7d5-d73c5d115aff","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.399336Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:bb9531cd328b3baf333f8fbe39aa17deebd5db082f993503e9a7c97f1e375364","observation_id":"2e9626f5-e131-4189-aecc-e10000c8562b","resolution":{"observed_at":"2026-08-12T12:12:26.017965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:26.000201Z","title":"Rodriguez, Development of a test section featuring a flat plate condi- tioned for the study of fully developed turbulent boundary layers using PIV, Ph.D","venue":null,"work_id":"f2ed99c1-5a52-4fe4-914d-f411e8dff1e2","year":2020},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.403382Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:acee51c47f185796753ae04b816254ec19253189d2fc0736599712b31869a86f","observation_id":"8f17ee4c-0115-49a5-a9cd-e56671be00d5","resolution":{"observed_at":"2026-08-12T12:12:26.004698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.986892Z","title":null,"venue":null,"work_id":"9221141d-aead-4fb1-b354-679bcd165f0a","year":2018},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.407464Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:22b9e9710c57b89864360768f3c308fc94d29569a3df532b1a5e115e5fdde8aa","observation_id":"ce457e42-c34b-4b4f-8883-dd05a0574633","resolution":{"observed_at":"2026-08-12T12:12:25.991085Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.973623Z","title":"Vreman, An eddy-viscosity subgrid-scale model for turbulent shear flow: Algebraic theory and applications, Physics of fluids 16 (2004) 3670–3681","venue":null,"work_id":"ea07a724-1b37-4510-9237-ce4199d0133f","year":2004},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.411561Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:8ddaeda3c0b9987db5190b954b01bd75d3a14bb160bedf80244ba9f39c55ba9e","observation_id":"efaeaf9f-5bfe-418a-95bf-9d0f29f568c1","resolution":{"observed_at":"2026-08-12T12:12:25.978120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.960214Z","title":null,"venue":null,"work_id":"581711ee-27a9-4e6e-829e-53a813f80706","year":1997},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.415930Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:b76d2c194413f4b0a6e0957f5b688e2262610ad1d21c190b2d8a2828b055bd0c","observation_id":"73b72ffc-c33b-4298-afae-90f96a67812c","resolution":{"observed_at":"2026-08-12T12:12:25.965079Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.946642Z","title":"Mani, Analysis and optimization of numerical sponge layers as a nonreflective boundary treatment, Journal of Computational Physics 231 (2012) 704–716","venue":null,"work_id":"d50cc111-56da-4106-941a-21363f829776","year":2012},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.420428Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:d01a20d0f78deef8408f3ad1dcb346067b8e45ec31aedc6617e5adc2ec13bf27","observation_id":"8410b088-17f1-446c-a674-819a1c07886a","resolution":{"observed_at":"2026-08-12T12:12:25.951105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.932254Z","title":"Zaman, Asymptotic spreading rate of initially compressible jets—experiment and analysis, Physics of Fluids 10 (1998) 2652–2660","venue":null,"work_id":"d0c84fb7-da65-49fd-953f-fffc51f94af7","year":1998},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.424469Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:59563ffc424b3e8e08d9e9957294a9e46ec54ccf55d5d2421358712ff855364e","observation_id":"72131e39-777b-4e49-82c7-8745d71e403a","resolution":{"observed_at":"2026-08-12T12:12:25.936938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.919465Z","title":"Zaman, Spreading characteristics of compressible jets from nozzles of various geometries, Journal of Fluid mechanics 383 (1999) 197–228","venue":null,"work_id":"d18ec2fd-13c3-4413-aaa8-ee70d113b63b","year":1999},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.428755Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:da9cc9733ca86453c9b9a1bf8cf45e9a5e296d36014a4e530fb592b5cd600c12","observation_id":"a9114a95-7e59-4f84-9b64-7a897b436158","resolution":{"observed_at":"2026-08-12T12:12:25.923586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.907055Z","title":null,"venue":null,"work_id":"e71221fd-a33a-4c7d-be4e-daa56e8e8a1a","year":2020},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.432925Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:8fab8aabc5234e6427567bd7a49240ecef7db6597ee0847689b9c1f3a79e9df5","observation_id":"40c1f9c7-e481-4e4d-8d24-5e4d08c2289a","resolution":{"observed_at":"2026-08-12T12:12:25.911232Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.894146Z","title":null,"venue":null,"work_id":"26d316eb-8a95-4b62-a820-b11ca8eb6e2c","year":1996},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.437227Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:4a001bbe87128ef129d2cd4ab5bf5981d73c6a1ddcab130994c520305a30a41a","observation_id":"26dd7783-e8ae-4914-b77d-48eb2ebe339f","resolution":{"observed_at":"2026-08-12T12:12:25.898342Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.881494Z","title":null,"venue":null,"work_id":"52958c25-d0a6-468f-9bf8-fb0e835591a4","year":null},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.441943Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:da4c465ca24878f190552e6c089e2221abaea48a673414968b718c749625c705","observation_id":"abb6b49f-65da-4330-a6d3-77af104fd6d7","resolution":{"observed_at":"2026-08-12T12:12:25.885586Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.867867Z","title":"Soft- ware available at https://modelflows.github.io/modelflowsapp/ (2023)","venue":null,"work_id":"ad1497c1-4bf5-45a4-8985-a72124460ad4","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.447362Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:c6d89805c53d6d508ec39de7d746af5ebc12a2fd1b51b78a1c3f04e180b18333","observation_id":"7613c6fb-4aa7-435c-9611-5db7bc49818e","resolution":{"observed_at":"2026-08-12T12:12:25.872848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.854523Z","title":null,"venue":null,"work_id":"9ea13375-0ce1-4f64-b28c-fc051eb09efa","year":null},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.451424Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:1b90ca7a8397ea653672e36ca66f2b2208970b49bb7b6a7d864a4a8805728b31","observation_id":"d891f6e5-9cca-4382-8ec3-0cebde4e45bb","resolution":{"observed_at":"2026-08-12T12:12:25.859094Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.841524Z","title":null,"venue":null,"work_id":"a84a8ace-2bf2-401d-94e1-6ff113d80f5f","year":null},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.455815Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:fb8303b888ea2ad671b11f480ba23a0fb1d1a51b52a751f63b436e93fabdd560","observation_id":"907aa0e6-59ce-4568-8c2f-afc024232616","resolution":{"observed_at":"2026-08-12T12:12:25.846056Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09791","last_updated":"2023-11-17T09:33:05Z","snapshot_observed_at":"2026-08-17T02:40:07.371498Z","submitted_at":"2023-11-16T11:14:54Z","title":"Low-cost singular value decomposition with optimal sensor placement","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.09791","snapshot_observed_at":"2026-08-12T12:12:25.460150Z","title":"Hetherington and S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.460150Z"},"links":{"cited_paper":"/paper/2311.09791","citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:97408eae2fbea67c3e5a73ba23313a27eac2d0bdf81fb4ddbfc3900e5d8be54a","observation_id":"fd1d8e81-26a1-44e2-84d0-36d2a894b3ee","resolution":{"observed_at":"2026-08-12T12:12:25.460150Z","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":"2026-08-12T12:12:25.828837Z","title":null,"venue":null,"work_id":"27628f70-2ade-4ab6-88fe-3c5a331edde6","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.464571Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:38aab9641de5d082249a2abe6f849d3185467140c682c51c7497b5fafeb67160","observation_id":"c0efc8be-edd4-42a7-8656-3edbfcaeee17","resolution":{"observed_at":"2026-08-12T12:12:25.832954Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.815555Z","title":null,"venue":null,"work_id":"9c7b3a11-fc6f-45be-ae21-3849193b4668","year":2023},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.468517Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:56ede545e1892319c35f4f9461f2fa318a5c8a130205a1073e859b15e5b70162","observation_id":"560860a0-2657-426c-a4d1-3b6ffd0ebb73","resolution":{"observed_at":"2026-08-12T12:12:25.819731Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.802087Z","title":"Iuliano, D","venue":null,"work_id":"7954d0fc-baf9-47f7-8cbc-0eb5f5efbfe2","year":2013},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.472938Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:c1c19602d11b5a56a600c7a189683e6dffec163bf43103bae376d86004062947","observation_id":"afbb78b0-6d5e-473e-9dc7-60500af5d3b1","resolution":{"observed_at":"2026-08-12T12:12:25.806700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.787253Z","title":"Freitag, B","venue":null,"work_id":"560458db-7b91-47cf-bff1-59fa7e016c4c","year":2018},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.476998Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:8705a559ca5ab71dcd4efe09f96392ecad58ae4295b8c1d066cc0280a648e64f","observation_id":"4ffdc02a-13d3-4878-81d3-69e28b9e01c5","resolution":{"observed_at":"2026-08-12T12:12:25.792373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.773920Z","title":null,"venue":null,"work_id":"ee953aed-f457-41f1-91b5-695e65b79b71","year":2019},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.481300Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:c656d835ca7dc5bafa9e1e1795dc8e21ebfef55ea20aa2fd2b5dc68e2f340e97","observation_id":"ad1c028d-ef24-47ec-b1b9-6af28ad8f3e0","resolution":{"observed_at":"2026-08-12T12:12:25.778370Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.759858Z","title":"Guemes, S","venue":null,"work_id":"b1714b14-db44-4a7d-86b2-55603989dbba","year":2019},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.485250Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:cbdce7a9fc0165830ae9ae80280c30e4fca47c29bfbae00ba3153b7965f9853f","observation_id":"9a0f1f51-84b9-4715-9438-f806e4448afa","resolution":{"observed_at":"2026-08-12T12:12:25.764316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.745617Z","title":"Discetti, M","venue":null,"work_id":"44703789-5559-4989-b8f7-a28083064928","year":2018},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.489285Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:86279835aa6471d8d2ac54db5474762f6939331ffb3dfcdef7ac9861bef28ad4","observation_id":"51da6212-907c-4d80-b5f4-45b5752455bb","resolution":{"observed_at":"2026-08-12T12:12:25.750548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.732889Z","title":"Guastoni, A","venue":null,"work_id":"535999ff-3af3-4720-bd05-51d9b80a64b4","year":2021},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.493394Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:41a761d99ba2508f3f9d4fbbd7b13872ca5e0f5e97929acdde2014d8ae4a67c4","observation_id":"d669710b-2fdd-4360-b74b-79040f9cacec","resolution":{"observed_at":"2026-08-12T12:12:25.737009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.719098Z","title":null,"venue":null,"work_id":"c496dd29-2cfe-4c38-b2e5-681d1bab77ed","year":1966},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.497357Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:cfa04f9201f49347d2b44ebbef1895eae8fc8e5af1cee3fba3a433df731a2875","observation_id":"d79ebf72-0308-44f5-829a-8fe0e1efbc71","resolution":{"observed_at":"2026-08-12T12:12:25.723149Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.705451Z","title":"De Lathawer, B","venue":null,"work_id":"4b168996-cc5c-488a-be04-e950ae85f094","year":2000},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.501429Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:73540796258da3defb37b1b1b4c9ef8a18da7c18b4641381a28756f3e7b23e6e","observation_id":"4abc7a97-9bef-4705-a1ca-6b52dd102a08","resolution":{"observed_at":"2026-08-12T12:12:25.709812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.692109Z","title":"De Lathawer, B","venue":null,"work_id":"52551d21-0b9e-4317-b91e-762d358ce78b","year":2000},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.505318Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:8839e71d977ca73dacc502482fc88b48167f06243b49d0621ee9010d4bf46a10","observation_id":"93cfd0f8-0c6a-4149-b98e-25436b401bf3","resolution":{"observed_at":"2026-08-12T12:12:25.696531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.678498Z","title":null,"venue":null,"work_id":"b1e80101-318d-47e0-82f0-c77ba1bd0140","year":2022},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.509400Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:7a90b022d251224874501bc6f14587484810f72c6f31aad30a7f7921c780ff86","observation_id":"50f409c3-6852-4811-aac0-16d29725440c","resolution":{"observed_at":"2026-08-12T12:12:25.683101Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T12:12:25.663723Z","title":null,"venue":null,"work_id":"faf3e647-1f43-4a51-bb7f-32e5815d2a92","year":2021},"citing_paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T12:12:25.513677Z"},"links":{"citing_paper":"/paper/2411.17433"},"observation_digest":"sha256:799f82c6702088bd10c4f13d77365e4fb7bcfe372ead7f1ee2ca04f741873642","observation_id":"580c4779-475f-453e-90b8-1636482a50b2","resolution":{"observed_at":"2026-08-12T12:12:25.668485Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.17433","last_updated":"2024-11-26T13:43:50Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-17T22:37:48.113282Z","submitted_at":"2024-11-26T13:43:50Z","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":5,"verified_fuzzy":40},"total_outbound_references":69},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2411.17433."}