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
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 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)
Download the high res and low res data and save the data files to the
subdirectory
modulus/examples/generative/diffusion/Kolmogorov_2D_data/.
Note: The directory from which the downloaded dataset files are loaded is specified in the configuration yaml files at
modulus/examples/generative/diffusion/Kolmogorov_2D_data/conf/. In the case when the default relative file location in a yaml file cannot be correctly recognized, please replace the relative location with the absolute location. For example, in the configuration file “modulus/examples/generative/diffusion/Kolmogorov_2D_data/conf/config_dfsr_train.yaml”, Line 24, the value of the key ‘data’ can be changed to an absolute file directory of the dataset file, e.g.,/<directory of modulus>/examples/generative/diffusion/Kolmogorov_2D_data/kf_2d_re1000_256_40seed.npy
Step 1 - Model Training
In directory modulus/examples/generative/diffusion/, run:
(without physics-informed conditioning)
python train.py --config-name=config_dfsr_train
or
(with physics-informed conditioning)
python train.py --config-name=config_dfsr_cond_train
Step 2 - Super-resolution
In directory modulus/examples/generative/diffusion/, run:
(without physics-informed conditioning)
python train.py --config-name=config_dfsr_generate
or
(with physics-informed conditioning)
python train.py --config-name=config_dfsr_cond_generate
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)