polymathic-ai / UNetClassic-convective_envelope_rsg

huggingface.co
Total runs: 103
24-hour runs: 0
7-day runs: 42
30-day runs: 49
Model's Last Updated: March 28 2025

Introduction of UNetClassic-convective_envelope_rsg

Model Details of UNetClassic-convective_envelope_rsg

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 convective_envelope_rsg of the Well, use the following commands.

from the_well.benchmark.models import UNetClassic

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

Runs of polymathic-ai UNetClassic-convective_envelope_rsg on huggingface.co

103
Total runs
0
24-hour runs
0
3-day runs
42
7-day runs
49
30-day runs

More Information About UNetClassic-convective_envelope_rsg huggingface.co Model

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UNetClassic-convective_envelope_rsg install

UNetClassic-convective_envelope_rsg is an open source model from GitHub that offers a free installation service, and any user can find UNetClassic-convective_envelope_rsg on GitHub to install. At the same time, huggingface.co provides the effect of UNetClassic-convective_envelope_rsg install, users can directly use UNetClassic-convective_envelope_rsg installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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