---
license: apache-2.0
language:
- en
tags:
- datasets
- machine-learning
- deep-learning
- physics-modeling
- scientific-ML
- material-point-method
- MPM
- smooth-particle-hydrodynamics
- SPH
- Lagrangian-dynamics
pretty_name: MPM-Verse-Large
size_categories:
- 100K<n<1M
---
# MPM-Verse-MaterialSim-Large

## Dataset Summary
This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, and jelly. 
Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based 
physical simulations. The dataset is rendered using five geometric models - Stanford-bunny, Spot, Dragon, Armadillo, and Blub. 
Each setting has 10 trajectories per object.

## Supported Tasks and Leaderboards
The dataset supports tasks such as:
- Physics-informed learning
- Point-cloud sequence prediction
- Fluid and granular material modeling
- Neural simulation acceleration

## Dataset Structure
### Materials and Metadata
Due to the longer duration, water and sand are split into multiple files for `rollout_full` and `train`. 
`rollout_full` represents the rollout trajectory over the full-order point-cloud, 
while `rollout` is on a sample size of 2600. 
The first 40 trajectories are used in the train set, and the remaining 10 are used in the test set.

### Dataset Characteristics
| Material  | # of Trajectories | Duration | Time Step (dt) | Shapes | Train Sample Size |
|-----------|------------------|----------|----------------|--------|------------------|
| Water3DNCLAW | 50 | 1000 | 5e-3 | Blub, Spot, Bunny, Armadillo, Dragon | 2600 |
| Sand3DNCLAW | 50 | 500 | 2.5e-3 | Blub, Spot, Bunny, Armadillo, Dragon | 2600 |
| Plasticine3DNCLAW | 50 | 200 | 2.5e-3 | Blub, Spot, Bunny, Armadillo, Dragon | 2600 |
| Jelly3DNCLAW | 50 | 334 | 7.5e-3 | Blub, Spot, Bunny, Armadillo, Dragon | 2600 |

### Dataset Files
Each dataset file is a dictionary with the following keys:

#### `train.obj/test.pt`
- `particle_type` (list): Indicator for material (only relevant for multimaterial simulations). Each element has shape `[N]` corresponding to the number of particles in the point-cloud.
- `position` (list): Snippet of past states, each element has shape `[N, W, D]` where:
  - `N`: Sample size
  - `W`: Time window (6)
  - `D`: Dimension (2D or 3D)
- `n_particles_per_example` (list): Integer `[1,]` indicating the size of the sample `N`
- `output` (list): Ground truth for predicted state `[N, D]`

#### `rollout.pt/rollout_full.pt`
- `position` (list): Contains a list of all trajectories, where each element corresponds to a complete trajectory with shape `[N, T, D]` where:
  - `N`: Number of particles
  - `T`: Rollout duration
  - `D`: Dimension (2D or 3D)

### Metadata Files
Each dataset folder contains a `metadata.json` file with the following information:
- `bounds` (list): Boundary conditions.
- `default_connectivity_radius` (float): Radius used within the graph neural network.
- `vel_mean` (list): Mean velocity of the entire dataset `[x, y, (z)]` for noise profiling.
- `vel_std` (list): Standard deviation of velocity `[x, y, (z)]` for noise profiling.
- `acc_mean` (list): Mean acceleration `[x, y, (z)]` for noise profiling.
- `acc_std` (list): Standard deviation of acceleration `[x, y, (z)]` for noise profiling.

## Downloading the Dataset
```python
from huggingface_hub import hf_hub_download, snapshot_download

files = ['train.obj', 'test.pt', 'rollout.pt', 'metadata.json', 'rollout_full.pt']

train_dir = hf_hub_download(repo_id=params.dataset_rootdir, repo_type='dataset', filename=os.path.join('Jelly3DNCLAW', files[0]), cache_dir="./dataset_mpmverse")
test_dir = hf_hub_download(repo_id=params.dataset_rootdir, repo_type='dataset', filename=os.path.join('Jelly3DNCLAW', files[1]), cache_dir="./dataset_mpmverse")
rollout_dir = hf_hub_download(repo_id=params.dataset_rootdir, repo_type='dataset', filename=os.path.join('Jelly3DNCLAW', files[2]), cache_dir="./dataset_mpmverse")
metadata_dir = hf_hub_download(repo_id=params.dataset_rootdir, repo_type='dataset', filename=os.path.join('Jelly3DNCLAW', files[3]), cache_dir="./dataset_mpmverse")
rollout_full_dir = hf_hub_download(repo_id=params.dataset_rootdir, repo_type='dataset', filename=os.path.join('Jelly3DNCLAW', files[4]), cache_dir="./dataset_mpmverse")

```

## Example Simulations

<table>
  <tr>
    <td><video width="250" controls><source src="https://huggingface.co/datasets/hrishivish23/MPM-Verse-MaterialSim-Large/blob/main/Viz/armadillo_plasticine.mp4" type="video/mp4">Your browser does not support the video tag.</video></td>
    <td><video width="250" controls><source src="video2.mp4" type="video/mp4">Your browser does not support the video tag.</video></td>
    <td><video width="250" controls><source src="video3.mp4" type="video/mp4">Your browser does not support the video tag.</video></td>
  </tr>
  <tr>
    <td><video width="250" controls><source src="video4.mp4" type="video/mp4">Your browser does not support the video tag.</video></td>
    <td><video width="250" controls><source src="video5.mp4" type="video/mp4">Your browser does not support the video tag.</video></td>
    <td><video width="250" controls><source src="video6.mp4" type="video/mp4">Your browser does not support the video tag.</video></td>
  </tr>
</table>


## Citation
If you use this dataset, please cite:

```bibtex
@article{viswanath2024reduced,
  title={Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs},
  author={Viswanath, Hrishikesh and Chang, Yue and Berner, Julius and Chen, Peter Yichen and Bera, Aniket},
  journal={arXiv preprint arXiv:2407.03925},
  year={2024}
}
```

## Source
The 3D datasets (e.g., Water3D, Sand3D, Plasticine3D, Jelly3D, RigidCollision3D, Melting3D) were generated using the NCLAW Simulator, developed by Ma et al. (ICML 2023).

```bibtex
@inproceedings{ma2023learning,
  title={Learning neural constitutive laws from motion observations for generalizable pde dynamics},
  author={Ma, Pingchuan and Chen, Peter Yichen and Deng, Bolei and Tenenbaum, Joshua B and Du, Tao and Gan, Chuang and Matusik, Wojciech},
  booktitle={International Conference on Machine Learning},
  pages={23279--23300},
  year={2023},
  organization={PMLR}
}
```