{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZHB52KBJ2PT66GMX7T6NYCU3OT","short_pith_number":"pith:ZHB52KBJ","schema_version":"1.0","canonical_sha256":"c9c3dd2829d3e7ef1997fcfcdc0a9b74e24b8b62778a42022c4fce27c2b5aef3","source":{"kind":"arxiv","id":"2312.05601","version":4},"attestation_state":"computed","paper":{"title":"A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","physics.flu-dyn"],"primary_cat":"math.NA","authors_text":"Han Zhang, Raymond Chan, Xue-Cheng Tai","submitted_at":"2023-12-09T16:05:33Z","abstract_excerpt":"Investigating blood flow in the cardiovascular system is crucial for assessing cardiovascular health. Computational approaches offer some non-invasive alternatives to measure blood flow dynamics. Numerical simulations based on traditional methods such as finite-element and other numerical discretizations have been extensively studied and have yielded excellent results. However, adapting these methods to real-life simulations remains a complex task. In this paper, we propose a method that offers flexibility and can efficiently handle real-life simulations. We suggest utilizing the physics-infor"},"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":"2312.05601","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2023-12-09T16:05:33Z","cross_cats_sorted":["cs.NA","physics.flu-dyn"],"title_canon_sha256":"7098722a82641b6911c4aea99745ed0e30d9c744adbfda5b92ed445a93753dfc","abstract_canon_sha256":"e2a7526972c6e6daabac0296072f92179c1e7d4630f56b60402fe8cc6c19c637"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:04.132140Z","signature_b64":"q+31phgeOnT7KYn3oCiMZhLmFh1UIk5/3c/YDQcgXjsFRBX0l1caL9oTGiYHznDo9HX19N4KME9sXA0ivtXqAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9c3dd2829d3e7ef1997fcfcdc0a9b74e24b8b62778a42022c4fce27c2b5aef3","last_reissued_at":"2026-07-05T08:28:04.131717Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:04.131717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","physics.flu-dyn"],"primary_cat":"math.NA","authors_text":"Han Zhang, Raymond Chan, Xue-Cheng Tai","submitted_at":"2023-12-09T16:05:33Z","abstract_excerpt":"Investigating blood flow in the cardiovascular system is crucial for assessing cardiovascular health. Computational approaches offer some non-invasive alternatives to measure blood flow dynamics. Numerical simulations based on traditional methods such as finite-element and other numerical discretizations have been extensively studied and have yielded excellent results. However, adapting these methods to real-life simulations remains a complex task. In this paper, we propose a method that offers flexibility and can efficiently handle real-life simulations. We suggest utilizing the physics-infor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.05601","kind":"arxiv","version":4},"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/2312.05601/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":"2312.05601","created_at":"2026-07-05T08:28:04.131774+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.05601v4","created_at":"2026-07-05T08:28:04.131774+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.05601","created_at":"2026-07-05T08:28:04.131774+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZHB52KBJ2PT6","created_at":"2026-07-05T08:28:04.131774+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZHB52KBJ2PT66GMX","created_at":"2026-07-05T08:28:04.131774+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZHB52KBJ","created_at":"2026-07-05T08:28:04.131774+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.02607","citing_title":"Towards Digital Twins for Optimal Radioembolization","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT","json":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT.json","graph_json":"https://pith.science/api/pith-number/ZHB52KBJ2PT66GMX7T6NYCU3OT/graph.json","events_json":"https://pith.science/api/pith-number/ZHB52KBJ2PT66GMX7T6NYCU3OT/events.json","paper":"https://pith.science/paper/ZHB52KBJ"},"agent_actions":{"view_html":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT","download_json":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT.json","view_paper":"https://pith.science/paper/ZHB52KBJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.05601&json=true","fetch_graph":"https://pith.science/api/pith-number/ZHB52KBJ2PT66GMX7T6NYCU3OT/graph.json","fetch_events":"https://pith.science/api/pith-number/ZHB52KBJ2PT66GMX7T6NYCU3OT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT/action/storage_attestation","attest_author":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT/action/author_attestation","sign_citation":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT/action/citation_signature","submit_replication":"https://pith.science/pith/ZHB52KBJ2PT66GMX7T6NYCU3OT/action/replication_record"}},"created_at":"2026-07-05T08:28:04.131774+00:00","updated_at":"2026-07-05T08:28:04.131774+00:00"}