Official repo for the paper GenCP: Towards Generative Modeling Paradigm of Coupled physics.
Tianrun Gao*, Haoren Zheng*, Wenhao Deng*, Haodong Feng, Tao Zhang, Ruiqi Feng, Qianyi Chen, Tailin Wu.
We introduce a novel framework for learning decoupled physics and generating coupled multi-physics systems using Conditional Flow Matching. Our method leverages Conditional Flow Matching (CFM) to learn joint distributions of coupled physical fields, enabling accurate and efficient generation of complex multi-physics phenomena.
Framework of paper:
Install dependencies:
conda create -n gencp python=3.10
conda activate gencp
pip install -r requirements.txt
pip install -e .
Alternatively, you can use the provided environment.yml:
conda env create -f environment.ymlAll datasets can be downloaded from this link.
- Double Cylinder
- NTcouple
- Turek-Hron
Use Double Cylinder as example.
- Set dataset path in config file or environment variable:
export DOUBLE_CYLINDER_DATA_ROOT=/path/to/double_cylinder/ - Run inference:
cd GenCP python infer_multi.py \ --config configs/double_cylinder/fsi_cno.yaml \ --fluid-checkpoint-path /path/to/fluid.pth \ --structure-checkpoint-path /path/to/structure.pth \ --num-sampling-steps 10 \ - View results in
./visualization_results/directory
- Prepare dataset and update
dataset_pathin config file - Start training:
cd GenCP python train.py --config configs/double_cylinder/fluid_cno.yaml - Monitor training: Checkpoints saved in
./results/double_cylinder/fluid_CNO/ - Evaluate: Use
infer_single.pywith trained checkpoint
NTcouple Multi-Field Inference:
bash scripts/ntcouple/our_cno/infer_ntcouple_multi.shNTcouple Single-Field Inference:
bash scripts/ntcouple/our_cno/infer_ntcouple_neutron.sh
bash scripts/ntcouple/our_cno/infer_ntcouple_solid.sh
bash scripts/ntcouple/our_cno/infer_ntcouple_fluid.sh- M2PDE: Diffusion-based approach for multi-physics modeling (baseline comparison, included in this repo)
If you find our work and/or our code useful, please cite us via:
@inproceedings{
anonymous2026gencp,
title={Gen{CP}: Towards Generative Modeling Paradigm of Coupled physics with Application to Fluid-Structure Interaction},
author={Anonymous},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=tn2VAi1KIO}
}This project is licensed under the MIT License - see the LICENSE file for details.
For questions and issues, please contact: [email protected]
