| --- |
| license: other |
| |
| tags: |
| - Lagrangian |
| - CFD |
| - graph neural network |
| language: |
| - en |
| - zh |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;"> Lagrangian </span> |
| </strong> |
| </p> |
| |
| ## Dataset Overview |
|
|
| The 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. |
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|
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| ## Supported Tasks |
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|
| 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. |
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|
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| ## Dataset Format and Structure |
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| 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 |
|
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| This dataset is compatible with the `OneScience/LagrangianMGN` model. |
|
|
| - Files and Download: |
|
|
| ```bash |
| 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 | |
|
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| ## Citation and License |
|
|
| - Original Lagrangian Water paper: [Learning to Simulate Complex Physics with Graph Networks](https://proceedings.mlr.press/v119/sanchez-gonzalez20a/sanchez-gonzalez20a.pdf) |
| - 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. |
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|
