Push model using huggingface_hub.

README.md

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+---
+datasets: polymathic-ai/planetswe
+tags:
+- physics
+---
+
+# Benchmarking Models on the Well
+
+[The Well](https://github.com/PolymathicAI/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](https://arxiv.org/abs/1505.04597).
+
+## 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)](https://huggingface.co/polymathic-ai/UNET-acoustic_scattering) | 1E-2 | 26 | 0.0395 |
+| [active_matter](https://huggingface.co/polymathic-ai/UNET-active_matter) | 5E-3 | 239 | 0.2609 |
+| [convective_envelope_rsg](https://huggingface.co/polymathic-ai/UNET-convective_envelope_rsg) | 5E-4 | 19 | 0.0701 |
+| [gray_scott_reaction_diffusion](https://huggingface.co/polymathic-ai/UNET-gray_scott_reaction_diffusion) | 1E-2 | 44 | 0.5870 |
+| [helmholtz_staircase](https://huggingface.co/polymathic-ai/UNET-helmholtz_staircase) | 1E-3 | 120 | 0.01655 |
+| [MHD_64](https://huggingface.co/polymathic-ai/UNET-MHD_64) | 5E-4 | 165 | 0.1988 |
+| [planetswe](https://huggingface.co/polymathic-ai/UNET-planetswe) | 1E-2 | 49 | 0.3498 |
+| [post_neutron_star_merger](https://huggingface.co/polymathic-ai/UNET-post_neutron_star_merger) | - | - | – |
+| [rayleigh_benard](https://huggingface.co/polymathic-ai/UNET-rayleigh_benard) | 1E-4 | 29 | 0.8448 |
+| [rayleigh_taylor_instability](https://huggingface.co/polymathic-ai/UNET-rayleigh_taylor_instability) | 5E-4 | 193 | 0.6140 |
+| [shear_flow](https://huggingface.co/polymathic-ai/UNET-shear_flow) | 5E-4 | 29 | 0.836 |
+| [supernova_explosion_64](https://huggingface.co/polymathic-ai/UNET-supernova_explosion_64) | 5E-4 | 46 | 0.3242 |
+| [turbulence_gravity_cooling](https://huggingface.co/polymathic-ai/UNET-turbulence_gravity_cooling) | 1E-3 | 14 | 0.3152 |
+| [turbulent_radiative_layer_2D](https://huggingface.co/polymathic-ai/UNET-turbulent_radiative_layer_2D) | 5E-3 | 500 | 0.2394 |
+| [viscoelastic_instability](https://huggingface.co/polymathic-ai/UNET-viscoelastic_instability) | 5E-4 | 198 | 0.3147 |
+
+
+## Loading the model from Hugging Face
+
+To load the UNetClassic model trained on the `planetswe` of the Well, use the following commands.
+
+```python
+from the_well.benchmark.models import UNetClassic
+
+model = UNetClassic.from_pretrained("polymathic-ai/UNetClassic-planetswe")
+```

config.json

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+{
+  "dim_in": 12,
+  "dim_out": 3,
+  "gradient_checkpointing": false,
+  "init_features": 48,
+  "n_spatial_dims": 2,
+  "spatial_resolution": [
+    256,
+    512
+  ]
+}

model.safetensors

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+oid sha256:522549d29b9abb80f121aa95bcbc16921a8c387141561c2daa5729597ec20488
+size 69908196