{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IWZJV5DME2U37MZXR2OIKC5WBP","short_pith_number":"pith:IWZJV5DM","schema_version":"1.0","canonical_sha256":"45b29af46c26a9bfb3378e9c850bb60bf7569f5ca1a6aa00653da680957cf6d7","source":{"kind":"arxiv","id":"2605.18340","version":1},"attestation_state":"computed","paper":{"title":"Physics Informed Neural Network-based Computational Method for Accelerating Time-Periodic Unsteady CFD Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients.","cross_cats":["physics.flu-dyn"],"primary_cat":"physics.comp-ph","authors_text":"Atul Sharma, Harshita Agarwal, Lakshya Chaplot","submitted_at":"2026-05-18T12:54:23Z","abstract_excerpt":"Presently, there is a steady state approach in Computational fluid dynamics (CFD) to obtain a steady solution directly from the steady state governing equations. Whereas, for obtaining a time-periodic flow solution, the present unsteady governing equations-based CFD approach starts from an initial condition and requires a large computational time during the initial non-periodic transient phase before reaching the periodic state. For obtaining the periodic flow directly, without transient simulations that may not be of interest, our objective is to propose a Physics Informed Neural Network (PIN"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.18340","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-05-18T12:54:23Z","cross_cats_sorted":["physics.flu-dyn"],"title_canon_sha256":"ba13e3fb7e68dde270a023f278144ca0860da454a37dfa7e4f74ace2b6514642","abstract_canon_sha256":"eb30467a8dcb464f91dc3ab654ff7e1198603b8f2add39544e4b498761f50fdf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:55.971635Z","signature_b64":"1IiviJTPlaJ0pj/lnVqwOEdrJN989iBwLbX6vMxugQRP+6PBDVc2lBVsUaUiynKFgyQPrUlmJJ6e4qjFBm/EDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45b29af46c26a9bfb3378e9c850bb60bf7569f5ca1a6aa00653da680957cf6d7","last_reissued_at":"2026-05-20T00:05:55.970857Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:55.970857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics Informed Neural Network-based Computational Method for Accelerating Time-Periodic Unsteady CFD Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients.","cross_cats":["physics.flu-dyn"],"primary_cat":"physics.comp-ph","authors_text":"Atul Sharma, Harshita Agarwal, Lakshya Chaplot","submitted_at":"2026-05-18T12:54:23Z","abstract_excerpt":"Presently, there is a steady state approach in Computational fluid dynamics (CFD) to obtain a steady solution directly from the steady state governing equations. Whereas, for obtaining a time-periodic flow solution, the present unsteady governing equations-based CFD approach starts from an initial condition and requires a large computational time during the initial non-periodic transient phase before reaching the periodic state. For obtaining the periodic flow directly, without transient simulations that may not be of interest, our objective is to propose a Physics Informed Neural Network (PIN"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results demonstrate that the PINN-based periodic solver takes substantially less computational time to achieve almost same accuracy as that obtained by the traditional transient-to-periodic solver.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a neural network optimized solely over one time period using a physics-informed loss can converge to the correct periodic solution without any dependence on or simulation of the transient evolution from initial conditions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6534f003ad49821cedb2017ad309074b42bbf8d24b87a50eb3fec698773f4f25"},"source":{"id":"2605.18340","kind":"arxiv","version":1},"verdict":{"id":"5de13e8e-e098-4ca3-98e7-f13707ead86f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T23:49:59.466005Z","strongest_claim":"Our results demonstrate that the PINN-based periodic solver takes substantially less computational time to achieve almost same accuracy as that obtained by the traditional transient-to-periodic solver.","one_line_summary":"A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a neural network optimized solely over one time period using a physics-informed loss can converge to the correct periodic solution without any dependence on or simulation of the transient evolution from initial conditions.","pith_extraction_headline":"Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients."},"integrity":{"clean":false,"summary":{"advisory":6,"critical":1,"by_detector":{"doi_compliance":{"total":7,"advisory":6,"critical":1,"informational":0}},"informational":0},"endpoint":"/pith/2605.18340/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1063/5.0203193/3281081.URL/aip/pof/article/36/4/041906/3281081/Computational-hemodynamics-and-hemoacoustic-study) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":2,"audited_at":"2026-05-20T00:02:34.956131Z","detected_doi":"10.1063/5.0203193/3281081.URL/aip/pof/article/36/4/041906/3281081/Computational-hemodynamics-and-hemoacoustic-study","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null},{"note":"Identifier '10.1007/978-3-030-72884-7/cover' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","detector":"doi_compliance","severity":"critical","ref_index":8,"audited_at":"2026-05-20T00:02:34.956131Z","detected_doi":"10.1007/978-3-030-72884-7/cover","finding_type":"unresolvable_identifier","verdict_class":"cross_source","detected_arxiv_id":null},{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/BF00350094/METRICS.URLhttps://link.springer.com/article/10.1007/BF00350094) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":10,"audited_at":"2026-05-20T00:02:34.956131Z","detected_doi":"10.1007/BF00350094/METRICS.URLhttps://link.springer.com/article/10.1007/BF00350094","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null},{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1063/5.0216266/3303820.URL/aip/pof/article/36/7/073620/3303820/Physics-informed-neural-networks-for-periodic) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":19,"audited_at":"2026-05-20T00:02:34.956131Z","detected_doi":"10.1063/5.0216266/3303820.URL/aip/pof/article/36/7/073620/3303820/Physics-informed-neural-networks-for-periodic","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null},{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/BF00625515/METRICS.URLhttps://link.springer.com/article/10.1007/BF0062551544) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":40,"audited_at":"2026-05-20T00:02:34.956131Z","detected_doi":"10.1007/BF00625515/METRICS.URLhttps://link.springer.com/article/10.1007/BF0062551544","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null},{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1115/1.4050542/1104439.URLhttps://dx.doi.org/10.1115/1.4050542) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":43,"audited_at":"2026-05-20T00:02:34.956131Z","detected_doi":"10.1115/1.4050542/1104439.URLhttps://dx.doi.org/10.1115/1.4050542","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null},{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/S00466-023-02334-7/FIGURES/29.URLhttps://link.springer.com/article/10.1007/s00466-023-02334-7) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":44,"audited_at":"2026-05-20T00:02:34.956131Z","detected_doi":"10.1007/S00466-023-02334-7/FIGURES/29.URLhttps://link.springer.com/article/10.1007/s00466-023-02334-7","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null}],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-20T00:02:34.956131Z","status":"completed","version":"1.0.0","findings_count":7},{"name":"doi_title_agreement","ran_at":"2026-05-20T00:01:20.426536Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:35.165185Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T23:21:58.830147Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"5e43a4cb79b07c440a9e0de8db2cf0d5c59d7f0952c28ddc697b34ed67f99c6b"},"references":{"count":46,"sample":[{"doi":"10.1098/rsta.2010.0355","year":2011,"title":"T. Colonius, D. R. Williams, Control of vortex shedding on two- and three-dimensional aerofoils, Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 369 (2011) 1525–","work_id":"a1ee4f22-0515-454a-b4a6-ecd5870a0846","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1063/5.0203193/3281081","year":2024,"title":"S. R. Morab, J. S. Murallidharan, A. Sharma, Computational hemodynamics and hemoacoustic study on carotid bifurcation: Effect of stenosis and branch angle, Physics of Fluids 36 (4 2024). doi:10.1063/5","work_id":"abc84c8e-de07-4f8d-8b20-d3125e02f5fc","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1155/2012/804765","year":2012,"title":"M. Mehrabi, S. Setayeshi, Computational fluid dynamics analysis of pulsatile blood flow behavior in modelled stenosed vessels with different severities, Mathematical Problems in Engineering 2012 (2012","work_id":"f79e1b36-c625-403f-8be0-5a528b76e7e4","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.2514/6","year":2001,"title":"In: 2018 AIAA Atmospheric Flight Mechanics Conference","work_id":"f6ce2e18-c36e-43a3-be82-f720bd7fe852","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.2166/aqua.2022.023","year":2022,"title":"Y. Cao, L. Zhou, C. Ou, H. Fang, D. Liu, 3d cfd simulation and analysis of transient flow in a water pipeline, Aqua Water Infrastructure, Ecosystems and Society 71 (2022) 751–767. doi:10.2166/AQUA.202","work_id":"14e43562-224a-4dd0-8538-bad7189d1170","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":46,"snapshot_sha256":"8bbf4d9f0011fa142f6e0f495a267bb219159dcb2d12647d52a7999bd27ea9e4","internal_anchors":1},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.18340","created_at":"2026-05-20T00:05:55.970991+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18340v1","created_at":"2026-05-20T00:05:55.970991+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18340","created_at":"2026-05-20T00:05:55.970991+00:00"},{"alias_kind":"pith_short_12","alias_value":"IWZJV5DME2U3","created_at":"2026-05-20T00:05:55.970991+00:00"},{"alias_kind":"pith_short_16","alias_value":"IWZJV5DME2U37MZX","created_at":"2026-05-20T00:05:55.970991+00:00"},{"alias_kind":"pith_short_8","alias_value":"IWZJV5DM","created_at":"2026-05-20T00:05:55.970991+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP","json":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP.json","graph_json":"https://pith.science/api/pith-number/IWZJV5DME2U37MZXR2OIKC5WBP/graph.json","events_json":"https://pith.science/api/pith-number/IWZJV5DME2U37MZXR2OIKC5WBP/events.json","paper":"https://pith.science/paper/IWZJV5DM"},"agent_actions":{"view_html":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP","download_json":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP.json","view_paper":"https://pith.science/paper/IWZJV5DM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18340&json=true","fetch_graph":"https://pith.science/api/pith-number/IWZJV5DME2U37MZXR2OIKC5WBP/graph.json","fetch_events":"https://pith.science/api/pith-number/IWZJV5DME2U37MZXR2OIKC5WBP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP/action/storage_attestation","attest_author":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP/action/author_attestation","sign_citation":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP/action/citation_signature","submit_replication":"https://pith.science/pith/IWZJV5DME2U37MZXR2OIKC5WBP/action/replication_record"}},"created_at":"2026-05-20T00:05:55.970991+00:00","updated_at":"2026-05-20T00:05:55.970991+00:00"}