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.
To load the UNetClassic model trained on the
active_matter
of the Well, use the following commands.
from the_well.benchmark.models import UNetClassic
model = UNetClassic.from_pretrained("polymathic-ai/UNetClassic-active_matter")
Runs of polymathic-ai UNetClassic-active_matter on huggingface.co
308
Total runs
0
24-hour runs
3
3-day runs
145
7-day runs
69
30-day runs
More Information About UNetClassic-active_matter huggingface.co Model
UNetClassic-active_matter huggingface.co
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UNetClassic-active_matter is an open source model from GitHub that offers a free installation service, and any user can find UNetClassic-active_matter on GitHub to install. At the same time, huggingface.co provides the effect of UNetClassic-active_matter install, users can directly use UNetClassic-active_matter installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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