---
license: cc-by-sa-4.0
task_categories:
- graph-ml
pretty_name: 2D dynamic non-linear structural mechanics with a non-linear non-local
  constitutive law
tags:
- physics learning
- geometry learning
dataset_info:
  features:
  - name: Base_2_2/Zone
    list:
      list: int64
  - name: Base_2_2/Zone/CellData/EROSION_STATUS
    list: float32
  - name: Base_2_2/Zone/CellData/EROSION_STATUS_times
    list: float64
  - name: Base_2_2/Zone/Elements_TRI_3/ElementConnectivity
    list: int64
  - name: Base_2_2/Zone/Elements_TRI_3/ElementRange
    list: int64
  - name: Base_2_2/Zone/GridCoordinates/CoordinateX
    list: float32
  - name: Base_2_2/Zone/GridCoordinates/CoordinateY
    list: float32
  - name: Base_2_2/Zone/PointData/U_x
    list: float32
  - name: Base_2_2/Zone/PointData/U_x_times
    list: float64
  - name: Base_2_2/Zone/PointData/U_y
    list: float32
  - name: Base_2_2/Zone/PointData/U_y_times
    list: float64
  splits:
  - name: train
    num_bytes: 12283129132
    num_examples: 1000
  - name: test
    num_bytes: 32615664
    num_examples: 18
  download_size: 7125438311
  dataset_size: 12315744796
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---
<p align='center'>
<img src='https://i.ibb.co/Xr6B25kd/PB-logo-2-D-Elasto-Plasto-Dynamics.png' alt='https://i.ibb.co/Xr6B25kd/PB-logo-2-D-Elasto-Plasto-Dynamics.png' width='1000'/>
<img src='https://i.ibb.co/FL7WhdWQ/2d-elasto-samples.gif' alt='https://i.ibb.co/FL7WhdWQ/2d-elasto-samples.gif' width='1000'/>
</p>

```yaml
data_production:
  physics: 2D dynamic non-linear structural mechanics, non-linear non-local constitutive
    law
  simulator: OpenRadioss
  type: simulation
legal:
  license: CC-by-SA 4.0
  owner: Safran
plaid:
  version: 0.1.10.dev114+gcbd3fd46f.d20251014

```
Example of commands:
```python
from datasets import load_dataset
from plaid.bridges import huggingface_bridge

repo_id = "chanel/dataset"
pb_def_name = "pb_def_name" #`pb_def_name` is to choose from the repo `problem_definitions` folder

# Load the dataset
hf_datasetdict = load_dataset(repo_id)

# Load addition required data
flat_cst, key_mappings = huggingface_bridge.load_tree_struct_from_hub(repo_id)
pb_def = huggingface_bridge.load_problem_definition_from_hub(repo_id, pb_def_name)

# Efficient reconstruction of plaid samples
for split_name, hf_dataset in hf_datasetdict.items():
    for i in range(len(hf_dataset)):
        sample = huggingface_bridge.to_plaid_sample(
            hf_dataset,
            i,
            flat_cst[split_name],
            key_mappings["cgns_types"],
        )

# Extract input and output features from samples:
for t in sample.get_all_mesh_times():
    for path in pb_def.get_in_features_identifiers():
        sample.get_feature_by_path(path=path, time=t)
    for path in pb_def.get_out_features_identifiers():
        sample.get_feature_by_path(path=path, time=t)
```
This dataset was generated in [PLAID](https://plaid-lib.readthedocs.io/), we refer to this documentation for additional details on how to extract data from `sample` objects.

### Dataset Sources

- **Papers:**
   - [arxiv](https://arxiv.org/abs/2505.02974)