AutoPanelImpact

Version: 1.0.0
Data type: finite-element simulation trajectories
Task: impact-conditioned displacement and shell von Mises effective-stress field prediction

AutoPanelImpact (Automotive Panel Impact) contains independent impact simulations on three automotive structural geometries. Each geometry has 500 Latin-hypercube-sampled impact conditions. Every case stores 17 aligned states of the full three-dimensional nodal displacement field and shell-element von Mises effective stress.

The dataset supports research on graph neural operators, mesh-based surrogate models, spatiotemporal field prediction, peak-event prediction, and simulation-based design screening.

Dataset summary

Geometry	Cases	Nodes	Directed graph edges	Shell elements	Displacement	von Mises effective stress
floorfrontdriver	500	7,408	29,572	7,374	[7408,17,3]	[7374,17]
floorfrontR	500	12,011	48,138	12,055	[12011,17,3]	[12055,17]
trunkfloor	500	14,440	58,074	14,589	[14440,17,3]	[14589,17]

The three geometries are independent datasets. Equal case identifiers across geometries do not denote paired physical simulations.

Stress definition

The effective_stress field is the shell-element von Mises equivalent stress exported from LS-PrePost. The LS-PrePost etime 9 component corresponds to Effective Stress (v-m), ip#max: for each shell element and retained state, the stored scalar is the maximum von Mises stress over all through-thickness integration points. The maximizing integration-point index is not retained. Stress values are in MPa, and the tensor shape is [Ne, 17].

Repository structure

AutoPanelImpact/
├── data/
│   ├── floorfrontdriver/cases_001_100.zip ... cases_401_500.zip
│   ├── floorfrontR/cases_001_100.zip ... cases_401_500.zip
│   └── trunkfloor/cases_001_100.zip ... cases_401_500.zip
├── meshes/
├── metadata/
├── scripts/
├── manifest.csv
├── checksums.sha256
├── DATASHEET.md
└── schema.json

Each ZIP member is stored as cases/caseNNN.pt. The files are PyTorch-serialized plain dictionaries. manifest.csv records the byte size and SHA-256 digest of every case.

Download

from huggingface_hub import snapshot_download

dataset_root = snapshot_download(
    repo_id="erichao123/AutoPanelImpact",
    repo_type="dataset",
    revision="v1.0.0",
)

Loading a case

PyTorch 2.6 or newer is recommended. The loader uses weights_only=True and reads cases directly from ZIP shards:

python scripts/load_case.py \
  --dataset-root . \
  --geometry floorfrontdriver \
  --case case001

from pathlib import Path
import sys

sys.path.insert(0, str(Path("scripts").resolve()))
from load_case import load_case, load_mesh

case = load_case(Path("."), "floorfrontdriver", "case001")
mesh = load_mesh(Path("."), "floorfrontdriver")
print(case["disp"].shape)
print(case["effective_stress"].shape)

Validation

python scripts/validate_dataset.py --dataset-root . --verify-checksums

The validator checks the case schema, tensor shapes, finite values, aligned time arrays, split coverage, mesh connectivity, archive membership, and SHA-256 digests.

Fixed split and peak-event task

The fixed split is 400 train / 50 validation / 50 test cases per geometry with seed 12345. Normalization statistics must be computed from the training cases only.

For peak-event prediction, the supplied script selects the state containing the global maximum valid nodal displacement magnitude and uses the von Mises effective-stress field from that same state.

Data-version note

The included floorfrontR data use source revision floorfrontR_lhs500_20260803. Files from earlier internal builds must not be mixed with this release; see metadata/floorfrontR_DATA_NOTE.md.

Limitations

• The fields are numerical simulation results, not physical crash-test measurements.
• The dataset covers three fixed meshes and their documented sampled conditions.
• It does not establish generalization to arbitrary vehicle geometries or real tests.
• Public test labels reproduce the fixed paper protocol but are not a hidden benchmark.

License

This repository is released under the MIT License. Third-party names and source model provenance are documented in THIRD_PARTY_NOTICES.md.

Citation

Please cite the versioned Hugging Face repository for release v1.0.0: https://huggingface.co/datasets/erichao123/AutoPanelImpact/tree/v1.0.0. Citation metadata is also provided in CITATION.cff.