polymathic-ai / UNetConvNext-planetswe

huggingface.co
Total runs: 5
24-hour runs: 0
7-day runs: 0
30-day runs: 1
Model's Last Updated: March 28 2025

Introduction of UNetConvNext-planetswe

Model Details of UNetConvNext-planetswe

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.

CNextU-Net

Implementation of the U-Net model using ConvNext blocks .

Model Details

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

Parameters Values
Spatial Filter Size 7
Initial Dimension 42
Block per Stage 2
Up/Down Blocks 4
Bottleneck Blocks 1
Trained Model Versions

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

Dataset Learning Rate Epoch VRMSE
acoustic_scattering_maze 1E-3 10 0.0196
active_matter 5E-3 156 0.0953
convective_envelope_rsg 1E-4 5 0.0663
gray_scott_reaction_diffusion 1E-4 15 0.3596
helmholtz_staircase 5E-4 47 0.00146
MHD_64 5E-3 59 0.1487
planetswe 1E-2 18 0.3268
post_neutron_star_merger - - -
rayleigh_benard 5E-4 12 0.4807
rayleigh_taylor_instability 5E-3 56 0.3771
shear_flow 5E-4 9 0.3972
supernova_explosion_64 5E-4 13 0.2801
turbulence_gravity_cooling 1E-3 3 0.2093
turbulent_radiative_layer_2D 5E-3 495 0.1247
viscoelastic_instability 5E-4 114 0.1966
Loading the model from Hugging Face

To load the UNetConvNext model trained on the planetswe of the Well, use the following commands.

from the_well.benchmark.models import UNetConvNext

model = UNetConvNext.from_pretrained("polymathic-ai/UNetConvNext-planetswe")

Runs of polymathic-ai UNetConvNext-planetswe on huggingface.co

5
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
1
30-day runs

More Information About UNetConvNext-planetswe huggingface.co Model

UNetConvNext-planetswe huggingface.co

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

UNetConvNext-planetswe huggingface.co Url

https://huggingface.co/polymathic-ai/UNetConvNext-planetswe

polymathic-ai UNetConvNext-planetswe online free

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

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https://huggingface.co/polymathic-ai/UNetConvNext-planetswe

UNetConvNext-planetswe install

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

UNetConvNext-planetswe install url in huggingface.co:

https://huggingface.co/polymathic-ai/UNetConvNext-planetswe

Url of UNetConvNext-planetswe

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