Diffusion-based-Fluid-Super-resolution

PyTorch implementation of

A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction

(Links to paper: Journal of Computational Physics | arXiv)

Sample 1

Sample 2

Overview

Denoising Diffusion Probablistic Models (DDPM) are a strong tool for data super-resolution and reconstruction. Unlike many other deep learning models which require a pair of low-res and high-res data for model training, DDPM is trained only on the high-res data. This feature is especially beneficial to reconstructing high-fidelity CFD data from low-fidelity reference, as it allows the model to be more independent of the low-res data distributions and subsequently more adaptive to various data patterns in different reconstruction tasks.

Datasets

Datasets used for model training and sampling can be downloaded via the following links.

• High resolution data (ground truth for the super-resolution task) (link)

• Low resolution data measured from random grid locations (input data for the super-resolution task) (link)

Running the Experiments

This code has been tested on the following environment:

python 3.8
PyTorch 1.7 + CUDA 10.1 + torchvision 0.8.2
TensorBoard 2.11
Numpy 1.22
tqdm 4.59
einops 0.4.1
matplotlib 3.6.2

Download the high res and low res data and save the data files to the subdirectory ./data/.

Step 1 - Model Training

In the subdirectory ./train_ddpm/, run:

bash train.sh

or

python main.py --config ./km_re1000_rs256_conditional.yml --exp ./experiments/km256/ --doc ./weights/km256/ --ni

The checkpoint of the trained model is by default saved at the following trajectory. You can atler the saving directory according to your need by changing the values of --exp and --doc.

.../Diffusion-based-Fluid-Super-resolution/train_ddpm/experiments/km256/logs/weights/km256/

Note: If you prefer not to go through Step 1, we provide the following pretrained checkpoints to directly start from Step 2:

1. model without physics-informed conditioning input (link)
2. model with physics-informed conditioning input (link)

Step 2 - Super-resolution

Add the model checkpoint file (e.g., baseline_ckpt.pth) from Step 1 to the following directory.

.../Diffusion-based-Fluid-Super-resolution/pretrained_weights/

In the main directory of this repo, run:

python main.py --config kmflow_re1000_rs256.yml --seed 1234 --sample_step 1 --t 240 --r 30

References

If you find this repository useful for your research, please cite the following work.

@article{shu2023physics,
  title={A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction},
  author={Shu, Dule and Li, Zijie and Farimani, Amir Barati},
  journal={Journal of Computational Physics},
  pages={111972},
  year={2023},
  publisher={Elsevier}
}

This implementation is based on / inspired by:

• https://github.com/ermongroup/SDEdit (SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations)
• https://github.com/ermongroup/ddim (Denoising Diffusion Implicit Models)