{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OBZZS4Y3GLW6SCD5SV54I7YTOJ","short_pith_number":"pith:OBZZS4Y3","schema_version":"1.0","canonical_sha256":"707399731b32ede9087d957bc47f13727ea9e6086e2973e4fe1f15d3e483fae1","source":{"kind":"arxiv","id":"2503.24074","version":1},"attestation_state":"computed","paper":{"title":"Physics-informed neural networks for hidden boundary detection and flow field reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Jian Deng, Weizheng Chen, Xin Bian, Yongzheng Zhu","submitted_at":"2025-03-31T13:30:46Z","abstract_excerpt":"Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics. This study presents a physics-informed neural network (PINN) framework designed to infer the presence, shape, and motion of static or moving solid boundaries within a flow field. By integrating a body fraction parameter into the governing equations, the model enforces no-slip/no-penetration boundary conditions in solid regions while preserving conservation laws of fluid dynamics. Using partial flow field data, the method simultaneous"},"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":"2503.24074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2025-03-31T13:30:46Z","cross_cats_sorted":["cs.LG","physics.comp-ph"],"title_canon_sha256":"7b053c7b5af87f9a367af4f2ce36d2a7796fee0fa23d47525c940d25a7dec07f","abstract_canon_sha256":"3336b07c04aa9d0355f52251f10371bed5468db49423634937a13f4a987fcb60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:04.104784Z","signature_b64":"2afPPAWOqZKmphROI6WRjhAKBYc8DykYy2IHiBm8b9G4ri78qHnkRVCwNP91Ad8RFoO7uQ7z5CBnf6y/l1baAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"707399731b32ede9087d957bc47f13727ea9e6086e2973e4fe1f15d3e483fae1","last_reissued_at":"2026-07-05T10:42:04.104388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:04.104388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-informed neural networks for hidden boundary detection and flow field reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Jian Deng, Weizheng Chen, Xin Bian, Yongzheng Zhu","submitted_at":"2025-03-31T13:30:46Z","abstract_excerpt":"Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics. This study presents a physics-informed neural network (PINN) framework designed to infer the presence, shape, and motion of static or moving solid boundaries within a flow field. By integrating a body fraction parameter into the governing equations, the model enforces no-slip/no-penetration boundary conditions in solid regions while preserving conservation laws of fluid dynamics. Using partial flow field data, the method simultaneous"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.24074","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/2503.24074/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":"2503.24074","created_at":"2026-07-05T10:42:04.104456+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.24074v1","created_at":"2026-07-05T10:42:04.104456+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.24074","created_at":"2026-07-05T10:42:04.104456+00:00"},{"alias_kind":"pith_short_12","alias_value":"OBZZS4Y3GLW6","created_at":"2026-07-05T10:42:04.104456+00:00"},{"alias_kind":"pith_short_16","alias_value":"OBZZS4Y3GLW6SCD5","created_at":"2026-07-05T10:42:04.104456+00:00"},{"alias_kind":"pith_short_8","alias_value":"OBZZS4Y3","created_at":"2026-07-05T10:42:04.104456+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/OBZZS4Y3GLW6SCD5SV54I7YTOJ","json":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ.json","graph_json":"https://pith.science/api/pith-number/OBZZS4Y3GLW6SCD5SV54I7YTOJ/graph.json","events_json":"https://pith.science/api/pith-number/OBZZS4Y3GLW6SCD5SV54I7YTOJ/events.json","paper":"https://pith.science/paper/OBZZS4Y3"},"agent_actions":{"view_html":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ","download_json":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ.json","view_paper":"https://pith.science/paper/OBZZS4Y3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.24074&json=true","fetch_graph":"https://pith.science/api/pith-number/OBZZS4Y3GLW6SCD5SV54I7YTOJ/graph.json","fetch_events":"https://pith.science/api/pith-number/OBZZS4Y3GLW6SCD5SV54I7YTOJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ/action/storage_attestation","attest_author":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ/action/author_attestation","sign_citation":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ/action/citation_signature","submit_replication":"https://pith.science/pith/OBZZS4Y3GLW6SCD5SV54I7YTOJ/action/replication_record"}},"created_at":"2026-07-05T10:42:04.104456+00:00","updated_at":"2026-07-05T10:42:04.104456+00:00"}