{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3AAMN4EBTROVWA7G7N7QMXO2KW","short_pith_number":"pith:3AAMN4EB","schema_version":"1.0","canonical_sha256":"d800c6f0819c5d5b03e6fb7f065dda55b1a622d24788ecfcada17aec2474a974","source":{"kind":"arxiv","id":"2503.17386","version":2},"attestation_state":"computed","paper":{"title":"A graph neural network surrogate model for mesh-based crashworthiness prediction of vehicle panel components","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Haoran Li, Haosu Zhou, Nan Li, Tobias Pfaff, Yingxue Zhao","submitted_at":"2025-03-16T23:55:40Z","abstract_excerpt":"Crashworthiness is a key performance measure in the design of safety-critical vehicle panel components such as B-pillars. Finite element (FE) simulations are widely used to evaluate crash responses but remain computationally expensive for large-scale, nonlinear impact scenarios, particularly when integrated into iterative design and optimisation processes. Although machine learning-based surrogate models have been developed for rapid crashworthiness analysis, they exhibit limitations in detailed representation of complex 3-dimensional components. Graph Neural Networks (GNNs) have emerged as a "},"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":"2503.17386","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-03-16T23:55:40Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"48bca7e2a5a38a641027f3be2634bdd469bc9c0bb15aaf4e51156fe06834c360","abstract_canon_sha256":"998616416eea8c9c11cc99cc60ad0be43175c7b7af89cf7b0c979e092e1efd89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:45.408977Z","signature_b64":"o0II7C6n2MmrALNqUI607JWCBPtg4Bc06MQiTobPGDZRUzzzypj3UtAAEKsGJc/a6Yr9bvs2sD8K6qZz67EzCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d800c6f0819c5d5b03e6fb7f065dda55b1a622d24788ecfcada17aec2474a974","last_reissued_at":"2026-06-19T16:12:45.408515Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:45.408515Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A graph neural network surrogate model for mesh-based crashworthiness prediction of vehicle panel components","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Haoran Li, Haosu Zhou, Nan Li, Tobias Pfaff, Yingxue Zhao","submitted_at":"2025-03-16T23:55:40Z","abstract_excerpt":"Crashworthiness is a key performance measure in the design of safety-critical vehicle panel components such as B-pillars. Finite element (FE) simulations are widely used to evaluate crash responses but remain computationally expensive for large-scale, nonlinear impact scenarios, particularly when integrated into iterative design and optimisation processes. Although machine learning-based surrogate models have been developed for rapid crashworthiness analysis, they exhibit limitations in detailed representation of complex 3-dimensional components. Graph Neural Networks (GNNs) have emerged as a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17386","kind":"arxiv","version":2},"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/2503.17386/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":"2503.17386","created_at":"2026-06-19T16:12:45.408572+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17386v2","created_at":"2026-06-19T16:12:45.408572+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17386","created_at":"2026-06-19T16:12:45.408572+00:00"},{"alias_kind":"pith_short_12","alias_value":"3AAMN4EBTROV","created_at":"2026-06-19T16:12:45.408572+00:00"},{"alias_kind":"pith_short_16","alias_value":"3AAMN4EBTROVWA7G","created_at":"2026-06-19T16:12:45.408572+00:00"},{"alias_kind":"pith_short_8","alias_value":"3AAMN4EB","created_at":"2026-06-19T16:12:45.408572+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2605.27758","citing_title":"High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2606.17577","citing_title":"Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07098","citing_title":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07098","citing_title":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","ref_index":91,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW","json":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW.json","graph_json":"https://pith.science/api/pith-number/3AAMN4EBTROVWA7G7N7QMXO2KW/graph.json","events_json":"https://pith.science/api/pith-number/3AAMN4EBTROVWA7G7N7QMXO2KW/events.json","paper":"https://pith.science/paper/3AAMN4EB"},"agent_actions":{"view_html":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW","download_json":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW.json","view_paper":"https://pith.science/paper/3AAMN4EB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17386&json=true","fetch_graph":"https://pith.science/api/pith-number/3AAMN4EBTROVWA7G7N7QMXO2KW/graph.json","fetch_events":"https://pith.science/api/pith-number/3AAMN4EBTROVWA7G7N7QMXO2KW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW/action/storage_attestation","attest_author":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW/action/author_attestation","sign_citation":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW/action/citation_signature","submit_replication":"https://pith.science/pith/3AAMN4EBTROVWA7G7N7QMXO2KW/action/replication_record"}},"created_at":"2026-06-19T16:12:45.408572+00:00","updated_at":"2026-06-19T16:12:45.408572+00:00"}