{"paper":{"title":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"CarCrashNet provides a validated open benchmark of 15,000 crash simulations and a neural model to predict full-vehicle responses.","cross_cats":["physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Dule Shu, Faez Ahmed, Matthew Klenk, Mohamed Elrefaie","submitted_at":"2026-05-08T01:28:12Z","abstract_excerpt":"Crash simulation is a cornerstone of modern vehicle development because it reduces the need for costly physical prototypes, accelerates safety-driven design iteration, and increasingly supports virtual testing workflows. At the same time, modeling structural crash mechanics remains exceptionally challenging: the response is governed by nonlinear contact, large deformation, material plasticity, failure, and complex multi-body interactions evolving over space and time on high-resolution finite-element meshes. In this work, we introduce CarCrashNet, a public high-fidelity open-source benchmark fo"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We introduce CarCrashNet, a public high-fidelity open-source benchmark for data-driven structural crash simulation... together with 825 full-vehicle crash simulations... We also introduce CrashSolver, a machine-learning model designed for full-vehicle crash prediction from high-resolution finite-element crash data.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the authors' open-source finite-element workflow based on OpenRadioss produces results sufficiently close to both experimental crash data and the commercial Ansys LS-DYNA solver for the generated dataset to serve as reliable training data for the neural solver.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CarCrashNet releases a large-scale open benchmark dataset of structural crash simulations and a hierarchical neural solver for data-driven full-vehicle crash prediction.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"CarCrashNet provides a validated open benchmark of 15,000 crash simulations and a neural model to predict full-vehicle responses.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"0f1983edd8b00e5f90d3276731068e9e471a4bde2d5fd6bf7b12c36b530977c4"},"source":{"id":"2605.07098","kind":"arxiv","version":2},"verdict":{"id":"652b641b-4097-464c-ac93-4a9d3e78b1de","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T00:50:28.612933Z","strongest_claim":"We introduce CarCrashNet, a public high-fidelity open-source benchmark for data-driven structural crash simulation... together with 825 full-vehicle crash simulations... We also introduce CrashSolver, a machine-learning model designed for full-vehicle crash prediction from high-resolution finite-element crash data.","one_line_summary":"CarCrashNet releases a large-scale open benchmark dataset of structural crash simulations and a hierarchical neural solver for data-driven full-vehicle crash prediction.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the authors' open-source finite-element workflow based on OpenRadioss produces results sufficiently close to both experimental crash data and the commercial Ansys LS-DYNA solver for the generated dataset to serve as reliable training data for the neural solver.","pith_extraction_headline":"CarCrashNet provides a validated open benchmark of 15,000 crash simulations and a neural model to predict full-vehicle responses."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.07098/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T17:31:18.572625Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T12:03:23.049594Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"f57e0b4692a3c523f72376ed158f80fffd6e6244c6c253456754dab96b5706c5"},"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"}