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OneScience-Group/lagrangian

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… See the full description on the dataset page: https://huggingface.co/datasets/OneScience-Group/lagrangian.

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Dataset Card

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-Group/LagrangianMGN model.

—Files and Download:

bash

hf download --dataset OneScience-Group/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.