---
license: apache-2.0
language:
- en
tags:
- OneScience
- fluid dynamics
- physics-informed neural networks
- long-horizon physical prediction
frameworks: PyTorch
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">PINNsformer</span>
  </strong>
</p>

# Model Overview

PINNsFormer is a Transformer-based physics-informed neural network framework developed by researchers at the Georgia Institute of Technology and Carnegie Mellon University. It enables rapid prediction of solutions to time-dependent partial differential equations and their associated physical fields.

Paper: [PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks](https://arxiv.org/abs/2307.11833).

# Model Description

PINNsFormer uses a Transformer encoder–decoder with multi-head attention to numerically solve time-dependent partial differential equations, including convection, reaction, wave, and Navier–Stokes equations.



# Use Cases

| Use Case | Description |
| :--- | :--- |
| Time-dependent PDE solving | Train a continuous-field surrogate constrained by physical residuals, boundary conditions, and initial conditions |
| Physics-informed neural network validation | Rapidly validate the PINNsFormer network, loss functions, weight serialization, and inference pipeline |
| One-dimensional reaction equation example | Generate target fields from an analytical solution for pipeline validation and error analysis |
| ModelScope/OneCode execution | Download the standalone model package, install its dependencies, and run the provided scripts |


# Usage

## 1. OneCode

Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience:

[Launch OneCode for one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Setup

**Hardware Requirements**

* A CPU can be used for small-scale pipeline validation.
* A GPU or DCU is recommended for training on larger grids or for more epochs.
* DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster, is recommended.

### Download the Model Package

```bash
modelscope download --model OneScience/PINNsformer --local_dir ./PINNsformer
cd PINNsformer
```

### Set Up the Runtime Environment


**DCU Environment**

```bash
# Activate DTK and Conda first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# Installation with uv is also supported
pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

**GPU Environment**
```bash
# Activate Conda first
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
# Installation with uv is also supported
pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

### Training Data

To use real data, download it from the link below and set `data.data_dir` in `conf/config.yaml` to the correct path.

| Source | Link | Extraction Code | Destination |
|---|---|---|---|
| Baidu Netdisk | https://pan.baidu.com/s/1pM4ICc6FJX5pLF7WEoozxQ?pwd=5gha | `5gha` | `convection/convection.mat` and `navier_stokes/cylinder_nektar_wake.mat` |

### Training

```bash
python scripts/train.py
```
The default configuration uses a smaller grid and fewer L-BFGS iterations for rapid end-to-end validation. To restore the scale of the original example, edit `conf/config.yaml`:

```yaml
data:
  x_num: 101
  t_num: 101

training:
  epochs: 500
```

### Model Weights
This repository will provide PINNsFormer model weights in the `weights/` directory. The weights will be uploaded soon.

### Inference

```bash
python scripts/inference.py
```

Inference loads `weight/1dreaction_pinnsformer.pt` and saves:

```text
result/prediction.npz
```

### Evaluation and Visualization

```bash
python scripts/result.py
```

# Official OneScience Resources

| Platform | OneScience 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 |

# Citations and License

* Original PINNsformer paper: [PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks](https://arxiv.org/abs/2307.11833).
* This repository retains the relevant source and attribution notices. Follow all applicable license requirements when using, modifying, or distributing its contents.