{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JRKJCT4ZTE6TH4V66DSLGFNDHS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ed0f3fbc1b2477e11e8061996296bc66fd5dac2f4e5e460069243be1d10ce146","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2026-05-09T09:32:51Z","title_canon_sha256":"5cc38d388f5b60c11c0731e59c49282c869b7c02629ec3f63088fd64a7967a3c"},"schema_version":"1.0","source":{"id":"2605.12544","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12544","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12544v1","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12544","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"pith_short_12","alias_value":"JRKJCT4ZTE6T","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"JRKJCT4ZTE6TH4V6","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"JRKJCT4Z","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:a283af7426249fda021ce314ded99f9e46daa0c8f2482ae8dcedb8488dbca575","target":"graph","created_at":"2026-05-18T03:10:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"The proposed DCP-INN model utilizes a diamond-shaped main network to capture low-frequency trends in physical evolution, and employs a parallel wide-deep correction network to compensate for high-frequency residuals resulting from complex geometric shapes... The results demonstrate that the method effectively mitigates optimization challenges and significantly reduces flow field reconstruction error."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the causal decoupling strategy, dual-network architecture, and Taylor-based high-order loss will reliably overcome severe optimization difficulties and generalization failures of standard PINNs specifically in highly tortuous intracranial geometries under extremely sparse data constraints."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DCP-INN combines a diamond-shaped main network for low-frequency flow trends with a parallel correction network for high-frequency residuals, plus a Taylor-expansion high-order loss, to reconstruct hemodynamics accurately from sparse data in tortuous vessels."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A dual-correction physics-informed neural network reconstructs accurate blood flow fields in tortuous intracranial arteries from sparse data."}],"snapshot_sha256":"cdc312db3985d27df97fb455d7c16779eb2293400e2239f8c5471fe4d2b584e3"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"77f312a6fc01a6aeffad773b00789ab34969f8157b8881cc89732c4b6909331b"},"paper":{"abstract_excerpt":"Quantifying hemodynamics in the curved segments of the intracranial internal carotid artery is a core challenge in diagnosing vascular stenosis. Conventional full-field imaging, such as 4D Flow MRI, is costly and difficult to widely promote. Meanwhile, reconstructing full-field fluid information from easily accessible and non-invasive sparse measurement data (such as transcranial Doppler ultrasound/computed tomography angiography) is essentially a highly challenging ill-posed inverse problem. To overcome the severe optimization difficulties and generalization failures of conventional physics-i","authors_text":"Hao Wu, Jingtai Song, Qinsheng Zhu, Xianwen Zhang, Xiaodong Xing, Yufeng Tang, Zhiyun Zhang","cross_cats":[],"headline":"A dual-correction physics-informed neural network reconstructs accurate blood flow fields in tortuous intracranial arteries from sparse data.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2026-05-09T09:32:51Z","title":"Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data"},"references":{"count":40,"internal_anchors":0,"resolved_work":40,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes,","work_id":"090c54ef-0b35-4fc2-9c36-55df2665be26","year":2009},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Blood vessel segmentation algorithms—Review of methods, datasets and evaluation metrics,","work_id":"b03e9804-2a71-4bdb-9fe5-f215a14f6c9b","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Fluid-structure interaction analysis of pulsatile flow in arterial aneurysms with physics-informed neural networks and computational fluid dynamics,","work_id":"4b4c5beb-30d6-442b-8449-da752978e565","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Automated generation of 0D and 1D reduced-order models of patient-specific blood flow,","work_id":"35a18734-2031-4c52-9c1e-9935c3f4cc51","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,","work_id":"7f6a4445-c2cb-4455-8d22-f65bc9439b97","year":2018}],"snapshot_sha256":"df1b7ef9c9c153afafa3d275057d47945a48dffe621f6a0dcf2ab48ea4d954d4"},"source":{"id":"2605.12544","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T21:55:30.612821Z","id":"9154bf5d-3f48-4e6f-9aec-b8a00dd44bad","model_set":{"reader":"grok-4.3"},"one_line_summary":"DCP-INN combines a diamond-shaped main network for low-frequency flow trends with a parallel correction network for high-frequency residuals, plus a Taylor-expansion high-order loss, to reconstruct hemodynamics accurately from sparse data in tortuous vessels.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A dual-correction physics-informed neural network reconstructs accurate blood flow fields in tortuous intracranial arteries from sparse data.","strongest_claim":"The proposed DCP-INN model utilizes a diamond-shaped main network to capture low-frequency trends in physical evolution, and employs a parallel wide-deep correction network to compensate for high-frequency residuals resulting from complex geometric shapes... The results demonstrate that the method effectively mitigates optimization challenges and significantly reduces flow field reconstruction error.","weakest_assumption":"That the causal decoupling strategy, dual-network architecture, and Taylor-based high-order loss will reliably overcome severe optimization difficulties and generalization failures of standard PINNs specifically in highly tortuous intracranial geometries under extremely sparse data constraints."}},"verdict_id":"9154bf5d-3f48-4e6f-9aec-b8a00dd44bad"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7a7f5a0d9309895f8eb590565380b74556c7fd55adc4599c2b988fe24f288d8d","target":"record","created_at":"2026-05-18T03:10:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ed0f3fbc1b2477e11e8061996296bc66fd5dac2f4e5e460069243be1d10ce146","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2026-05-09T09:32:51Z","title_canon_sha256":"5cc38d388f5b60c11c0731e59c49282c869b7c02629ec3f63088fd64a7967a3c"},"schema_version":"1.0","source":{"id":"2605.12544","kind":"arxiv","version":1}},"canonical_sha256":"4c54914f99993d33f2bef0e4b315a33c9ac96a32dc40721a66bccec235aeeee4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4c54914f99993d33f2bef0e4b315a33c9ac96a32dc40721a66bccec235aeeee4","first_computed_at":"2026-05-18T03:10:02.274961Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:10:02.274961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QQXfRCBlZ5iulD/Esdv1In9K1knFO07y50qGtzeMKp3V1s1Y4TC6cR6OrtTnr/ZjkYseGGFgRdXIRNt/EhwpBg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:10:02.275550Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12544","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7a7f5a0d9309895f8eb590565380b74556c7fd55adc4599c2b988fe24f288d8d","sha256:a283af7426249fda021ce314ded99f9e46daa0c8f2482ae8dcedb8488dbca575"],"state_sha256":"b208a1109f9b55f56c2a1644714116d51ffdac2c5f353412435aa2d97b41c006"}