OneScience-Group/lagrangian

1---2license: other3#User-Defined Tags4tags:5  - Lagrangian6  - CFD7  - graph neural network8language:9  - en10  - zh11---12<p align="center">13  <strong>14    <span style="font-size: 30px;"> Lagrangian </span>15  </strong>16</p>17 18## Dataset Overview19 20The Lagrangian dataset is sourced from the DeepMind team's ICML 2020 paper [Learning to Simulate Complex Physics with Graph Networks](https://proceedings.mlr.press/v119/sanchez-gonzalez20a.html). 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.21 22 23## Supported Tasks24 25This 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.26 27 28## Dataset Format and Structure29 30The 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`.31 32| Feature | Component | shape | dtype | Source Encoding | Description |33|---|---|---:|---|---|---|34| `position` | sequence feature | `[1001, num_particles, 2]` | `float32` | `bytes` | Two-dimensional particle positions for 1,001 frames, corresponding to 1,000 evolution time steps |35| `particle_type` | context feature | `[num_particles]` | `int64` | `bytes` | Type identifier for each particle |36 37The data splits are as follows:38 39| File | split | Number of Trajectories | Description |40|---|---|---:|---|41| `data/Water/train.tfrecord` | `train` | 1,000 | Used for model training |42| `data/Water/valid.tfrecord` | `valid` | 30 | Used for model validation and hyperparameter selection |43| `data/Water/test.tfrecord` | `test` | 30 | Used for model testing and result evaluation |44 45`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`.46 47## How to Use the Dataset48 49This dataset is compatible with the `OneScience-Group/LagrangianMGN` model.50 51- Files and Download:52 53```bash54hf download --dataset OneScience-Group/lagrangian --local-dir ./data55```56 57 58## Official OneScience Information59 60| Platform | OneScience Main Repository | Skills Repository |61|---|---|---|62| Gitee  | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |63| GitHub  | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |64 65## Citation and License66 67- Original Lagrangian Water paper: [Learning to Simulate Complex Physics with Graph Networks](https://proceedings.mlr.press/v119/sanchez-gonzalez20a/sanchez-gonzalez20a.pdf)68- 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.69