This is a collaborative work between the ETH Computational Applied Mathematics Lab and the ETH Soft Robotics Lab.
For ease of use, we provide an Anaconda enviroment with all necessary packages to run all of our code. Install and activate it using:
conda env create -f environment.yml
conda activate dse
The data for the Burgers and Shear Layer experiment can be downloaded from the python files available in _Data/Burgers and _Data/Humidity. The data for the Humidity experiment may be downloaded from the shell script in _Data/Humidity, this requires a NASA EarthData account to download. Further instructions for getting started with NASA EarthData are available at this link: https://www.earthdata.nasa.gov/. The data for the Airfoil and Elasticity experiments are provided by Li et al., Fourier Neural Operator with Learned Deformations for PDEs on General Topologies and are available in this Google Drive.
We formatted the models to be run easily from the train.py script. Select the model you wish to run (options fno, ffno, ufno, geo_fno, geo_ffno, geo_ufno, fno_dse, ffno_dse, ufno_dse) and the according experiments (options Burgers, Elasticity, Airfoil, ShearLayer, Humidity). Some experiments will require certain models to be used, for example, point-cloud data such as airfoil or elasticity require geo_fno or one of its variants as opposed to fno.
The best hyperparameters are chosen by default, when the configs in train.py are left empty. These best parameters are defined in the separate model architectures of each experiment. You can choose to overwrite these by adding corresponding entries in the configs.
The code for the spherical shallow water equations are based on a different setup, which can be run directly from the files (fno.py,sfno.py,fno_dse.py,fno_dse.py)
Running hyperparameter_sweep.py with your desired parameters in the configs will grid search for the best architecture. Outputs are written to a .txt file in _Models.