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
Contact3DNCLAW	50	600	2.5e-3	Blub, Spot, Bunny	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

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")

Processing Train

import torch
import pickle

with open("path/to/train.obj", "rb") as f:
  data = pickle.load(f)

positions = data["position"][0]
print(positions.shape)  # Example output: (N, W, D)

Processing Rollout

import torch
import pickle

with open("path/to/rollout_full.obj", "rb") as f:
  data = pickle.load(f)

positions = data["position"]
print(len(positions))  # Example output: 50
print(positions.shape) # Example output: (N, T, 3)

Example Simulations

Citation

If you use this dataset, please cite:

@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).