---
license: apache-2.0
language:
- en
tags:
- OneScience
- fluid-dynamics
- shallow-water-equations
frameworks: PyTorch
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">PINN-SWE-CircularDamBreak</span>
  </strong>
</p>

# Model Introduction

PINN-SWE-CircularDamBreak reproduces the dCDB9 circular dam-break experiment for the two-dimensional shallow water equations. It predicts water depth and velocity without labeled training fields.

Paper: [Physics-Informed Neural Networks for Solving the Two-Dimensional Shallow Water Equations with Terrain Topography and Rainfall Source Terms](https://arxiv.org/pdf/2501.11372)

# Model Description

The network maps `(x, y, t)` to `(h, u, v)`. It has five fully connected hidden layers of width 300 with N-LAAF activations. Training uses shallow-water PDE residuals, initial conditions, and periodic boundary conditions.

## Applications

| Application | Description |
| :--- | :--- |
| Circular dam-break prediction | Predict water depth and velocity over time |
| Shallow-water solver verification | Compare PINN fields with a numerical reference |

# Usage

## 1. OneCode

[Open the OneCode AI4S environment](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Setup

**Hardware**

- Formal training, inference, and evaluation were verified on one DCU compute node; the training script requires a Slurm DCU job environment.
- At least 30 GB of host memory is recommended.
- CPU is suitable for configuration inspection, not the recorded full training run.

### Download the model

```bash
hf download OneScience-Group/PINN-SWE-CircularDamBreak --local-dir ./PINN-SWE-CircularDamBreak
cd PINN-SWE-CircularDamBreak
```

### Install dependencies

**DCU**

```bash
# Activate a compatible DTK and Conda environment first.
conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
pip install numpy PyYAML matplotlib
```

**GPU**

```bash
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
pip install numpy PyYAML matplotlib
```

The formal scripts were verified only in a Slurm DCU environment. The GPU command is provided for dependency setup, not as a tested training claim.

### Training data

No external dataset is needed. `scripts/fake_data.py` creates collocation points and targets from the configuration's PDE, initial condition, and periodic boundary conditions.

```bash
python scripts/fake_data.py --config config/config.yaml
```

### Train

The downloaded package includes a trained checkpoint. To train from scratch, first archive the bundled weights, then run on an allocated Slurm DCU node:

```bash
mkdir -p weight/pretrained
mv weight/best_model.pth weight/last_model.pth weight/pretrained/
python scripts/train.py --config config/config.yaml
```

The formal run takes 5,000 optimizer steps and writes `weight/best_model.pth`.

### Trained weights

`weight/best_model.pth` was selected at step 4,100 after the full 5,000-step run and is ready for inference. File size: 1,466,146 bytes. SHA256: `26953fa8d218a585b05dd2775e1d3574a86ec33a2d68fb2bd58c1c78a8b3734e`.

### Inference

```bash
python scripts/inference.py --config config/config.yaml
```

### Evaluation and visualization

```bash
python scripts/result.py --config config/config.yaml
```

Outputs are written to `results/`. At `t=0.8 s`, the recorded water-depth RMSE is `0.01066` versus `0.01000` in paper Table 2. The recorded metric uses an independently implemented HLL reference, so it is not strict parity against the paper's exact reference array.

# OneScience Repositories

| Platform | 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

- Paper: [Physics-Informed Neural Networks for Solving the Two-Dimensional Shallow Water Equations with Terrain Topography and Rainfall Source Terms](https://arxiv.org/pdf/2501.11372).
- Authors' open repository: [PINN_SWE_open](https://github.com/tianyongsen/PINN_SWE_open), licensed under Apache License 2.0. This package is an independent reproduction and retains attribution.