license: other
tags:
- Lagrangian
- CFD
- graph neural network
language:
- en
- zh
Lagrangian
Dataset Overview
The Lagrangian dataset is sourced from the DeepMind team's ICML 2020 paper Learning to Simulate Complex Physics with Graph Networks. It consists of the two-dimensional Water particle-dynamics data from the paper's Graph Network-based Simulator (GNS) benchmark. The data represents particles as graph nodes and describes fluid evolution over time through particle-position sequences and particle types. It can be used for Lagrangian particle-dynamics modeling, long-horizon fluid rollout prediction, and evaluation of graph-neural-network physics simulations.
Supported Tasks
This standardized dataset repository organizes the training, validation, and test TFRecord files for Lagrangian Water, together with data metadata, a data schema, an integrity summary, and validation scripts. It can be used as data input for training, inference, evaluation, and visualization with the OneScience/LagrangianMGN model. The core data is placed uniformly under data/Water/; the training set contains 1,000 trajectories, while the validation and test sets each contain 30 trajectories.
Dataset Format and Structure
The dataset uses the TFRecord SequenceExample format, with each record corresponding to one particle-motion trajectory. The number of particles, num_particles, varies by trajectory, and the spatial dimension is dim=2.
| Feature | Component | shape | dtype | Source Encoding | Description |
|---|---|---|---|---|---|
position |
sequence feature | [1001, num_particles, 2] |
float32 |
bytes |
Two-dimensional particle positions for 1,001 frames, corresponding to 1,000 evolution time steps |
particle_type |
context feature | [num_particles] |
int64 |
bytes |
Type identifier for each particle |
The data splits are as follows:
| File | split | Number of Trajectories | Description |
|---|---|---|---|
data/Water/train.tfrecord |
train |
1,000 | Used for model training |
data/Water/valid.tfrecord |
valid |
30 | Used for model validation and hyperparameter selection |
data/Water/test.tfrecord |
test |
30 | Used for model testing and result evaluation |
data/Water/metadata.json also contains bounds=[[0.1, 0.9], [0.1, 0.9]], sequence_length=1000, default_connectivity_radius=0.015, dt=0.0025, and normalization fields such as vel_mean, vel_std, acc_mean, and acc_std.
How to Use the Dataset
This dataset is compatible with the OneScience/LagrangianMGN model.
- Files and Download:
hf download --dataset OneScience-Sugon/lagrangian --local-dir ./data
Official OneScience Information
| Platform | OneScience Main Repository | Skills Repository |
|---|---|---|
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
Citation and License
- Original Lagrangian Water paper: Learning to Simulate Complex Physics with Graph Networks
- This dataset is organized from the Learning to Simulate project released by Google DeepMind. Before public distribution, confirm the licensing requirements of the upstream project.