| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - computational-fluid-dynamics |
| - natural-convection |
| datasets: |
| - NurshatMenglik/NeuralConvection_2D_TrainTest |
| --- |
| |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">NeuralConvection-2D-CompactFNO</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
|
|
| NeuralConvection-2D-CompactFNO is a surrogate for two-dimensional natural convection. It maps velocity, temperature, and pressure fields from a Boussinesq simulation to the corresponding compressible-flow fields. |
|
|
| Paper: [A Neural Surrogate Approach for Simulating Natural Convection Problems](https://arxiv.org/abs/2606.25259) |
|
|
| # Model Description |
|
|
| This package reproduces the compact FNO in Table 5: four Fourier blocks, width 64, and 16 retained spectral modes per direction. Inputs and outputs are four-channel `64x64` fields in `[u_x, u_y, T, p]` order. The model was trained for 200 epochs on the authors' stochastic-temperature-boundary dataset. |
|
|
| ## Use Cases |
|
|
| | Scenario | Description | |
| | :--- | :--- | |
| | Natural-convection correction | Predict compressible fields from Boussinesq fields | |
| | Industrial thermal-flow simulation | Accelerate field estimation in two-dimensional coupled flow and heat-transfer studies | |
|
|
| # Usage |
|
|
| ## 1. OneCode |
|
|
| [Open the OneCode AI4S environment](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
|
| ## 2. Manual Setup |
|
|
| **Hardware requirements** |
|
|
| - GPU or DCU is recommended. CPU is suitable for small import and forward checks. |
| - For DCU, load the matching DTK environment before running PyTorch. |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| hf download OneScience-Group/NeuralConvection-2D-CompactFNO --local-dir ./NeuralConvection-2D-CompactFNO |
| cd NeuralConvection-2D-CompactFNO |
| ``` |
|
|
| ### Install the Runtime |
|
|
| **DCU** |
|
|
| ```bash |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| pip install scikit-image PyYAML |
| ``` |
|
|
| **GPU** |
|
|
| ```bash |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| pip install scikit-image PyYAML |
| ``` |
|
|
| ### Training Data |
|
|
| The authors' [NeuralConvection 2D dataset](https://huggingface.co/datasets/NurshatMenglik/NeuralConvection_2D_TrainTest) provides 4,000 training pairs and three 200-pair test splits. Download the fixed `v1.0-legacy` revision used for this reproduction: |
|
|
| ```bash |
| hf download NurshatMenglik/NeuralConvection_2D_TrainTest --repo-type dataset --revision v1.0-legacy --local-dir ./data |
| ``` |
|
|
| The public package sets `data.root_dir` in `config/config.yaml` to `./data`, so the commands below use the downloaded data when run from the project directory. |
|
|
| ### Training |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| The best checkpoint is saved to `weight/best_model.pth`. |
|
|
| ### Model Weights |
|
|
| The package includes the best checkpoint from a complete 200-epoch DCU training run. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| # OneScience Repositories |
|
|
| | Platform | Main 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 | |
|
|
| # Citation and License |
|
|
| - Paper: [A Neural Surrogate Approach for Simulating Natural Convection Problems](https://arxiv.org/pdf/2606.25259). |
| - This reproduction package is released under Apache-2.0. The authors' dataset is CC BY 4.0 and requires attribution; the official research code uses the NCSA license. |
|
|
