license: apache-2.0
language:
- en
tags:
- OneScience
- computational-fluid-dynamics
- natural-convection
datasets:
- NurshatMenglik/NeuralConvection_2D_TrainTest
NeuralConvection-2D-CompactFNO
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
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
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
hf download OneScience-Group/NeuralConvection-2D-CompactFNO --local-dir ./NeuralConvection-2D-CompactFNO
cd NeuralConvection-2D-CompactFNO
Install the Runtime
DCU
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
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 provides 4,000 training pairs and three 200-pair test splits. Download the fixed v1.0-legacy revision used for this reproduction:
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
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
python scripts/inference.py
Evaluation and Visualization
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.
- 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.