{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FXV6E3MSW7KSRNTLY57SQWHSKH","short_pith_number":"pith:FXV6E3MS","schema_version":"1.0","canonical_sha256":"2debe26d92b7d528b66bc77f2858f251e034d776dac61f69a91121db1048fbfd","source":{"kind":"arxiv","id":"2309.14722","version":1},"attestation_state":"computed","paper":{"title":"Physics-informed neural network to augment experimental data: an application to stratified flows","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Adrien Lefauve, Lu Zhu, P. F. Linden, Rich R. Kerswell, Xianyang Jiang","submitted_at":"2023-09-26T07:29:42Z","abstract_excerpt":"We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data and apply it to stratified flows. The PINN is a fully-connected deep neural network fed with time-resolved, three-component velocity fields and density fields measured simultaneously in three dimensions at $Re = O(10^3)$ in a stratified inclined duct experiment. The PINN enforces incompressibility, the governing equations for momentum and buoyancy, and the boundary conditions by automatic differentiation. The physics-constrained, augmented data are output at an increased spatio-tempo"},"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":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2309.14722","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2023-09-26T07:29:42Z","cross_cats_sorted":[],"title_canon_sha256":"fe9a7793c85f6161cba4c65b7081dfd0f5a57816e467cbbe3f9ba653df7a97e8","abstract_canon_sha256":"c6ecd84d7b321e0a703f566df1e6d0d3702db74eb109e158b05792b934033901"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:29.136202Z","signature_b64":"BuQVXb4fLTdQDTeuuOq434vJxtwZwmJNtVaxl6qtO33guH6MM0qCNVh27HXYo0TqJYcNdESLsWDr2U48RGCuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2debe26d92b7d528b66bc77f2858f251e034d776dac61f69a91121db1048fbfd","last_reissued_at":"2026-07-05T06:54:29.135660Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:29.135660Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-informed neural network to augment experimental data: an application to stratified flows","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Adrien Lefauve, Lu Zhu, P. F. Linden, Rich R. Kerswell, Xianyang Jiang","submitted_at":"2023-09-26T07:29:42Z","abstract_excerpt":"We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data and apply it to stratified flows. The PINN is a fully-connected deep neural network fed with time-resolved, three-component velocity fields and density fields measured simultaneously in three dimensions at $Re = O(10^3)$ in a stratified inclined duct experiment. The PINN enforces incompressibility, the governing equations for momentum and buoyancy, and the boundary conditions by automatic differentiation. The physics-constrained, augmented data are output at an increased spatio-tempo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14722","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.14722/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"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":"2309.14722","created_at":"2026-07-05T06:54:29.135719+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.14722v1","created_at":"2026-07-05T06:54:29.135719+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14722","created_at":"2026-07-05T06:54:29.135719+00:00"},{"alias_kind":"pith_short_12","alias_value":"FXV6E3MSW7KS","created_at":"2026-07-05T06:54:29.135719+00:00"},{"alias_kind":"pith_short_16","alias_value":"FXV6E3MSW7KSRNTL","created_at":"2026-07-05T06:54:29.135719+00:00"},{"alias_kind":"pith_short_8","alias_value":"FXV6E3MS","created_at":"2026-07-05T06:54:29.135719+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/FXV6E3MSW7KSRNTLY57SQWHSKH","json":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH.json","graph_json":"https://pith.science/api/pith-number/FXV6E3MSW7KSRNTLY57SQWHSKH/graph.json","events_json":"https://pith.science/api/pith-number/FXV6E3MSW7KSRNTLY57SQWHSKH/events.json","paper":"https://pith.science/paper/FXV6E3MS"},"agent_actions":{"view_html":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH","download_json":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH.json","view_paper":"https://pith.science/paper/FXV6E3MS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.14722&json=true","fetch_graph":"https://pith.science/api/pith-number/FXV6E3MSW7KSRNTLY57SQWHSKH/graph.json","fetch_events":"https://pith.science/api/pith-number/FXV6E3MSW7KSRNTLY57SQWHSKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH/action/storage_attestation","attest_author":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH/action/author_attestation","sign_citation":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH/action/citation_signature","submit_replication":"https://pith.science/pith/FXV6E3MSW7KSRNTLY57SQWHSKH/action/replication_record"}},"created_at":"2026-07-05T06:54:29.135719+00:00","updated_at":"2026-07-05T06:54:29.135719+00:00"}