Pith Number

pith:FBFLWD2B

pith:2026:FBFLWD2BXCQO3HNDUHUUQD7PHJ

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CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation

Dule Shu, Faez Ahmed, Matthew Klenk, Mohamed Elrefaie

CarCrashNet provides a validated open benchmark of 15,000 crash simulations and a neural model to predict full-vehicle responses.

arxiv:2605.07098 v2 · 2026-05-08 · cs.LG · physics.comp-ph

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest 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.

C2weakest 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.

C3one 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.

Receipt and verification

First computed	2026-05-20T00:03:14.656093Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

284abb0f41b8a0ed9da3a1e9480fef3a7bb16df60ab8bc644b6f29e0bc5fdfe4

Aliases

arxiv: 2605.07098 · arxiv_version: 2605.07098v2 · doi: 10.48550/arxiv.2605.07098 · pith_short_12: FBFLWD2BXCQO · pith_short_16: FBFLWD2BXCQO3HND · pith_short_8: FBFLWD2B

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FBFLWD2BXCQO3HNDUHUUQD7PHJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 284abb0f41b8a0ed9da3a1e9480fef3a7bb16df60ab8bc644b6f29e0bc5fdfe4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "104df8c1fd844dd420a3afde9c80ee6439d89a9ff18710535e02a27935cdca71",
    "cross_cats_sorted": [
      "physics.comp-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-08T01:28:12Z",
    "title_canon_sha256": "4584469987a7c00c9052c1275380c9183276e1abf714254dd31b78cccb2a73c9"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.07098",
    "kind": "arxiv",
    "version": 2
  }
}