OneScience/GP_for_TO

GP_for_TO

Model Overview

GP_for_TO (Physics-informed GP-TO) is a physics-informed Gaussian-process framework for topology optimization developed by researchers at Northwestern University. It jointly optimizes material distributions and physical state variables in complex design domains without requiring an explicit mesh discretization.

Paper: Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes https://arxiv.org/abs/2408.03490

Model Description

GP_for_TO uses a Gaussian-process architecture whose mean function is parameterized by a deep neural network. It provides a mesh-free approach to fluid topology optimization problems such as minimizing dissipated power in Stokes flow.

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 GPU or DCU is recommended.
• A CPU can be used for import checks and small-scale pipeline validation, but full training and inference will be slow.
• 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/GP_for_TO --local_dir ./GP_for_TO
cd GP_for_TO

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

The method does not rely on pregenerated topology structures or labeled physical fields. Instead, it samples spatial points within the design domain and constructs physics-informed training signals from the governing Stokes-flow equations, boundary conditions, dissipated-power objective, and material volume-fraction constraint. The paper evaluates four fluid topology optimization cases—Rugby, Pipe Bend, Diffuser, and Double Pipe—and compares the results against COMSOL solutions obtained with the SIMP method.

Training

The default training configuration retains the original settings: N_col_domain=10000, N_train_per_BC=25, num_iter=50000, and diff_method=Numerical.

python scripts/train.py

Model Weights

This repository will provide GP_for_TO model weights in the weights/ directory. The weights will be uploaded soon.

Inference

python scripts/inference.py

By default, inference loads weight/gp_for_to.pt and produces:

result/inference/predictions.npz
result/inference/inference_summary.json

predictions.npz contains five arrays: x, u, v, p, and ro.

Result 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 GP_for_TO paper: Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes.
• This repository retains the relevant source and attribution notices. Follow all applicable license requirements when using, modifying, or distributing its contents.