polymathic-ai / TFNO-rayleigh_taylor_instability

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30-day runs: -1
Model's Last Updated: March 28 2025

Introduction of TFNO-rayleigh_taylor_instability

Model Details of TFNO-rayleigh_taylor_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.

Tensorized Fourier Neural Operator

Implementation of the Tensorized Fourier Neural Operator provided by neuraloperator v0.3.0 .

Model Details

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

Parameters Values
Modes 16
Blocks 4
Hidden Size 128
Trained Model Versions

Below is the list of checkpoints available for the training of TFNO on different datasets of the Well.

Dataset Learning Rate Epoch VRMSE
acoustic_scattering_maze 1E-3 27 0.5034
active_matter 1E-3 243 0.3342
convective_envelope_rsg 1E-3 13 0.0195
gray_scott_reaction_diffusion 5E-3 45 0.1784
helmholtz_staircase 5E-4 131 0.00031
MHD_64 1E-3 155 0.3347
planetswe 5E-4 49 0.1061
post_neutron_star_merger 5E-4 99 0.4064
rayleigh_benard 1E-4 31 0.8568
rayleigh_taylor_instability 1E-4 175 0.2251
shear_flow 1E-3 24 0.3626
supernova_explosion_64 1E-4 35 0.3645
turbulence_gravity_cooling 5E-4 10 0.2789
turbulent_radiative_layer_2D 1E-3 500 0.4938
viscoelastic_instability 5E-3 199 0.7021
Loading the model from Hugging Face

To load the TFNO model trained on the rayleigh_taylor_instability of the Well, use the following commands.

from the_well.benchmark.models import TFNO

model = TFNO.from_pretrained("polymathic-ai/TFNO-rayleigh_taylor_instability")

Runs of polymathic-ai TFNO-rayleigh_taylor_instability on huggingface.co

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

TFNO-rayleigh_taylor_instability huggingface.co Url

https://huggingface.co/polymathic-ai/TFNO-rayleigh_taylor_instability

polymathic-ai TFNO-rayleigh_taylor_instability online free

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https://huggingface.co/polymathic-ai/TFNO-rayleigh_taylor_instability

TFNO-rayleigh_taylor_instability install

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

TFNO-rayleigh_taylor_instability install url in huggingface.co:

https://huggingface.co/polymathic-ai/TFNO-rayleigh_taylor_instability

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