| --- |
| license: unknown |
| language: |
| - en |
| tags: |
| - OneScience |
| - fluid dynamics |
| - topology optimization |
| - Gaussian processes |
| - physics-informed learning |
| frameworks: PyTorch |
| --- |
| |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">GP_for_TO</span> |
| </strong> |
| </p> |
| |
| # 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](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
|
| ## 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 |
|
|
| ```bash |
| hf download OneScience-Group/GP_for_TO --local-dir ./GP_for_TO |
| cd GP_for_TO |
| ``` |
|
|
| ### 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 |
| 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`. |
|
|
| ```bash |
| 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 |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| By default, inference loads `weight/gp_for_to.pt` and produces: |
|
|
| ```text |
| result/inference/predictions.npz |
| result/inference/inference_summary.json |
| ``` |
|
|
| `predictions.npz` contains five arrays: `x`, `u`, `v`, `p`, and `ro`. |
|
|
| ## Result 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 GP_for_TO paper: [Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes](https://arxiv.org/abs/2408.03490). |
| - This repository retains the relevant source and attribution notices. Follow all applicable license requirements when using, modifying, or distributing its contents. |
|
|
