polymathic-ai / UNetClassic-viscoelastic_instability

huggingface.co
Total runs: 12
24-hour runs: 0
7-day runs: 1
30-day runs: 0
Model's Last Updated: March 28 2025

Introduction of UNetClassic-viscoelastic_instability

Model Details of UNetClassic-viscoelastic_instability

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 viscoelastic_instability of the Well, use the following commands.

from the_well.benchmark.models import UNetClassic

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

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

12
Total runs
0
24-hour runs
1
3-day runs
1
7-day runs
0
30-day runs

More Information About UNetClassic-viscoelastic_instability huggingface.co Model

UNetClassic-viscoelastic_instability huggingface.co

UNetClassic-viscoelastic_instability huggingface.co is an AI model on huggingface.co that provides UNetClassic-viscoelastic_instability's model effect (), which can be used instantly with this polymathic-ai UNetClassic-viscoelastic_instability model. huggingface.co supports a free trial of the UNetClassic-viscoelastic_instability model, and also provides paid use of the UNetClassic-viscoelastic_instability. Support call UNetClassic-viscoelastic_instability model through api, including Node.js, Python, http.

UNetClassic-viscoelastic_instability huggingface.co Url

https://huggingface.co/polymathic-ai/UNetClassic-viscoelastic_instability

polymathic-ai UNetClassic-viscoelastic_instability online free

UNetClassic-viscoelastic_instability huggingface.co is an online trial and call api platform, which integrates UNetClassic-viscoelastic_instability's modeling effects, including api services, and provides a free online trial of UNetClassic-viscoelastic_instability, you can try UNetClassic-viscoelastic_instability online for free by clicking the link below.

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https://huggingface.co/polymathic-ai/UNetClassic-viscoelastic_instability

UNetClassic-viscoelastic_instability install

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

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https://huggingface.co/polymathic-ai/UNetClassic-viscoelastic_instability

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