license: apache-2.0
language:
- en
tags:
- OneScience
- fluid dynamics
- physics-informed neural networks
- long-horizon physical prediction
frameworks: PyTorch
PINNsformer
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.
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
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
modelscope download --model OneScience/PINNsformer --local_dir ./PINNsformer
cd PINNsformer
Set Up the Runtime Environment
DCU Environment
# 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
# 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
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:
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
python scripts/inference.py
Inference loads weight/1dreaction_pinnsformer.pt and saves:
result/prediction.npz
Evaluation and Visualization
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.
- This repository retains the relevant source and attribution notices. Follow all applicable license requirements when using, modifying, or distributing its contents.