StructBench/notch-beam-2d-impact

NotchBeam2D-Impact — StructBench canonical dataset

Download

One case, one file — fetch exactly what you need (pip install huggingface_hub):

from huggingface_hub import hf_hub_download, snapshot_download

# one case
path = hf_hub_download("StructBench/notch-beam-2d-impact",
                       filename="<case_id>.h5", repo_type="dataset")

# the full archive (resumable; cached under HF_HOME)
root = snapshot_download("StructBench/notch-beam-2d-impact", repo_type="dataset")

cases.csv lists every case with its split and loading/geometry parameters plus a SHA-256 manifest; pin the dataset repo's v0.1.0 tag (revision="v0.1.0" — a data release, independent of the code version) for reproducible pipelines. Point structbench-train --data-root at the snapshot directory. Code, benchmark protocol, and leaderboards: <https://github.com/qilinli/StructBench>.

Autoregressive next-step surrogate of a 2D SPH notched concrete beam under drop-weight impact (ADR-0026). Covers 3 beam widths, 3 impactor shapes, 3 notch positions, and 4 velocities. Three bodies: the K&C concrete beam (part 1) is the predicted deformable; the steel impactor (part 2) and the two support blocks (part 3) are protocol-kinematic (ADR-0026) — driven by ground truth during rollout (both move: the impactor decelerates from its case velocity to ~10-20% on contact, the supports displace a few mm), excluded from the training loss and from position/strain metrics, with both QoIs restricted to concrete particles.

Dataset summary

• —Solver: LS-DYNA (SPH; erosion: no)
• —Loading: drop-weight impact, initial velocity 40-160 m/s, impactor cross-sections plate/disk/rod (case names Rectangular/Sphere/Bullet)
• —Geometry: 2D SPH notched beam, H 80 x W {320,480,640} mm
• —Materials: MAT_CONCRETE_DAMAGE_REL3 (K&C; density 2.4e-6 kg/mm3); MATPLASTICKINEMATIC
• —Source units: kg-mm-ms (files are strict SI, ADR-0012)
• —Cases: 110 (train 88, val 8, test_interp 12, probe 2)
• —Particles per case: 4264-12966; 502 frames at 0.001 ms; 24.9 GB on disk
• —Fields: node/displacement, node/velocity, node/acceleration, sph/stress, sph/strain, sph/strainrate, sph/effectiveplasticstrain, sph/pressure, sph/density, sph/internalenergy, sph/mass, sph/radius, sph/nneighbors, sph/deletion, global/kineticenergy, global/internalenergy, global/totalenergy
• —Provenance: LS-DYNA parametric sweep (3 widths x 3 shapes x 3 notches x 4 velocities) produced by Curtin collaborators — extends the published 81-specimen drop-weight study (plate/disk/rod impactors at 80/120/160 m/s) with a 40 m/s velocity level; benchmark protocol per ADR-0026.
• —License: CC BY 4.0

Files

• —<case_id>.h5 — one HDF5 file per case; the file name is the case id (layout below).
• —card.json — machine-readable card metadata (ADR-0027): the facts above plus the split sizes.
• —README.md — this file; LICENSE-*.txt — the data licence (CC BY 4.0).

Manifest and input decks (Hugging Face mirror)

• —cases.csv — one row per .h5: case_id, split (held_aside for files shipped outside the protocol splits), the loading/geometry parameters parsed from the id, n_nodes (rows of nodes/coords, so including any boundary-shell nodes), n_frames (stored frames), file_bytes, sha256 (integrity manifest; also what the Dataset Viewer shows).
• —decks/<case_id>.k — the LS-DYNA input deck of every case (also embedded verbatim in each file's metadata/source_deck); re-running a deck regenerates the raw output the adapter converts to canonical HDF5.
• —Case ids: NB-I-<W>-<Shape>-<n>-<V> — beam width W mm (height 80 mm), impactor Rectangular / Sphere / Bullet (plate / disk / rod cross-section), notch position code n ∈ {a, b, c}, impact speed V m/s; the two off-grid probes are S_<H>_<W>_V<V>_<label> (beam height H mm, width W mm, disk impactor at V m/s).

HDF5 layout

One HDF5 file per case, readable with h5py or any HDF5 tool. Every quantity is stored in strict SI (m, s, kg, Pa, J) regardless of the solver's kg-mm-ms source convention. Small scalars are HDF5 attributes; arrays are datasets (float64 geometry and time, float32 response, int64 ids, variable-length UTF-8 strings — h5py returns those as bytes); response arrays are gzip-compressed and chunked in blocks of frames, so slicing along the frame axis reads only the chunks it touches. Shapes below use N nodes, P SPH particles, E elements, T stored frames and d = metadata.dimension; the exact schema version is the schema_version attribute (ADR-0013 — 0.2.0 readers read 0.1.0 files unchanged, ADR-0042). ADR-NNNN refers to the decision records under decisions/ in the code repository.

sph/stress and sph/strain are 6-component Voigt tensors; scalar targets are loader-derived (see the card's aux field).

Loading

Plain HDF5 — nothing beyond h5py is needed:

import h5py

with h5py.File("<case_id>.h5") as f:
    t = f["response/time/t"][:]                # (T,) s
    x0 = f["nodes/coords"][:]                  # (N, d) m
    u = f["response/node/displacement"]        # (T, N, d) m, chunked along T
    u_last = u[-1]                             # one frame, no full read
    sig = f["response/element/sph/stress"][:]  # (T, P, 6) Pa, Voigt

Or through StructBench's loader, which returns the ML working frame (positions in mm; max_principal_strain, dimensionless) with the auxiliary target derived on the fly — P SPH particles only (boundary-shell nodes are dropped); T′ = T − 1: the terminal solver-output frame is dropped (ADR-0028):

from structbench.datasets import load_case_trajectory

traj = load_case_trajectory("<case_id>.h5", aux_field="max_principal_strain")
traj.positions   # (T′, P, d) float32, mm
traj.aux         # (T′, P) float32, dimensionless
traj.time        # (T′,) float64, s

Benchmark protocol

This archive backs the NotchBeam2D-Impact benchmark in StructBench. Task: autoregressive transition (ADR-0026); auxiliary target max_principal_strain (dimensionless); 6 input frames, horizon frames [6, 250) of 502 scored (250 µs, ADR-0039); full-length diagnostic, scored at native output times; quantities of interest: midspandeflectionpeak, crackedfraction. The full evaluation protocol and its rationale, the baseline recipes and checkpoints, and the current leaderboard live on the benchmark page in the code repository — <https://github.com/qilinli/StructBench/blob/main/docs/benchmarks/notchbeam2dimpact.md> — so the numbers have a single home. To train a baseline on this archive:

pip install git+https://github.com/qilinli/StructBench # or: pip install -e .
structbench-train --mode train --config configs/notch_beam_2d_impact/cgn.toml \
    --data-root /path/to/this/folder --out runs/notch_beam_2d_impact-cgn

References

• —MGN — Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., & Battaglia, P. W. (2021). Learning Mesh-Based Simulation with Graph Networks. ICLR. https://arxiv.org/abs/2010.03409
• —CGN — Li, Q., Wang, Z., Li, L., Hao, H., Chen, W., & Shao, Y. (2023). Machine learning prediction of structural dynamic responses using graph neural networks. Computers & Structures, 289, 107188. https://doi.org/10.1016/j.compstruc.2023.107188
• —Transolver — Wu, H., Luo, H., Wang, H., Wang, J., & Long, M. (2024). Transolver: A Fast Transformer Solver for PDEs on General Geometries. ICML. https://arxiv.org/abs/2402.02366
• —Transolver++ — Luo, H., Wu, H., Zhou, H., Wang, J., & Long, M. (2025). Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries. https://arxiv.org/abs/2502.02414. Adapted per ADR-0057 (thuml reference implementation github.com/thuml/Transolver_plus).
• —GeoFLARE — Adams, R., et al. (NVIDIA). GeoTransolver. arXiv:2512.20399; with Puri, R., et al. FLARE: Fast Low-rank Attention Routing Engine. arXiv:2508.12594. GeoFLARE is GeoTransolver with the FLARE attention backend (attentiontype GALEFA; ADR-0045).

Citation

The data and the code are released together — cite the software (CITATION.cff in the code repository):

@software{structbench,
  author  = {Li, Qilin},
  title   = {StructBench: standardized benchmarks for machine learning on structural simulation},
  year    = {2026},
  version = {0.3.0},
  url     = {https://github.com/qilinli/StructBench},
}

Licence: CC BY 4.0 — when redistributing or building on the data, credit Qilin Li (Curtin University) / StructBench and link this dataset repository.