polymathic-ai/UNetClassic-shear_flow

Benchmarking Models on the Well

The Well is a 15TB dataset collection of physics simulations. This model is part of the models that have been benchmarked on the Well.

The models have been trained for a fixed time of 12 hours or up to 500 epochs, whichever happens first. The training was performed on a NVIDIA H100 96GB GPU. In the time dimension, the context length was set to 4. The batch size was set to maximize the memory usage. We experiment with 5 different learning rates for each model on each dataset. We use the model performing best on the validation set to report test set results.

The reported results are here to provide a simple baseline. They should not be considered as state-of-the-art. We hope that the community will build upon these results to develop better architectures for PDE surrogate modeling.

U-Net

Implementation of the U-Net model.

Model Details

For benchmarking on the Well, we used the following parameters.

Parameters	Values
Spatial Filter Size	3
Initial Dimension	48
Block per Stage	1
Up/Down Blocks	4
Bottleneck Blocks	1

Trained Model Versions

Below is the list of checkpoints available for the training of U-Net on different datasets of the Well.

Dataset	Learning Rate	Epochs	VRMSE
acoustic_scattering_maze	1E-2	26	0.0395
active_matter	5E-3	239	0.2609
convective_envelope_rsg	5E-4	19	0.0701
gray_scott_reaction_diffusion	1E-2	44	0.5870
helmholtz_staircase	1E-3	120	0.01655
MHD_64	5E-4	165	0.1988
planetswe	1E-2	49	0.3498
post_neutron_star_merger	-	-	–
rayleigh_benard	1E-4	29	0.8448
rayleigh_taylor_instability	5E-4	193	0.6140
shear_flow	5E-4	29	0.836
supernova_explosion_64	5E-4	46	0.3242
turbulence_gravity_cooling	1E-3	14	0.3152
turbulent_radiative_layer_2D	5E-3	500	0.2394
viscoelastic_instability	5E-4	198	0.3147

Loading the model from Hugging Face

To load the UNetClassic model trained on the shear_flow of the Well, use the following commands.

from the_well.benchmark.models import UNetClassic

model = UNetClassic.from_pretrained("polymathic-ai/UNetClassic-shear_flow")