hrishivish23/MPM-Verse-MaterialSim-Small

Dataset Card for MPMVerse Physics Simulation Dataset

Dataset Summary

This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, elasticity, jelly, rigid collisions, and melting. Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based physical simulations.

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

The dataset consists of the following materials, each with its own characteristics:

Material	Num Trajectories	Duration	Full-order Size	Sample Size	Train Size
Elasticity3D	8	120	78,391	2K	20,520
Plasticine3D	15	320	5,035	1K	37,680
Sand3D_Long	5	300	32,793	1K	14,112
Water3D_Long	5	1000	55,546	1.5K	47,712
Sand3D	15	320	7,146	1.5K	37,680
Water3D	15	320	2,979	0.7K	37,680
Jelly3D	15	200	1,659	0.5K	46,560
Collisions3D	15	500	8,019	3K	29,640
MultiMaterial2D	-	1000	1.5K	-	50,745
WaterDrop2D	-	1000	1K	-	99,500
Sand2D	-	320	2K	-	94,500

Data Instances

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)

Note: particle_type, n_particles_per_example, and output in rollout.pt/rollout_full.pt are not relevant and should be ignored.

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.

Note: sequence_length, dim, and dt can be ignored.

How to Use

Example Usage

from datasets import load_dataset

# Load the dataset from Hugging Face
dataset = load_dataset("hrishivish23/MPM-Verse-MaterialSim-Small", data_dir=".")

Processing Examples

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)

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

This dataset aggregates Material Point Method (MPM) simulations from two primary sources:

• 2D Simulations

  • The 2D datasets (e.g., Water2D, Sand2D, MultiMaterial2D) are derived from Sánchez-González et al. (ICML 2020), which introduced the use of Graph Neural Networks (GNNs) for learning physics-based simulations.

• 3D Simulations

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

Citations

@inproceedings{sanchez2020learning,
  title={Learning to simulate complex physics with graph networks},
  author={Sanchez-Gonzalez, Alvaro and Godwin, Jonathan and Pfaff, Tobias and Ying, Rex and Leskovec, Jure and Battaglia, Peter},
  booktitle={International Conference on Machine Learning},
  pages={8459--8468},
  year={2020},
  organization={PMLR}
}

@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}
}