OneScience/PDENNEval

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.