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Push model using huggingface_hub.

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  1. README.md +66 -0
  2. config.json +14 -0
  3. model.safetensors +3 -0
README.md ADDED
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+ ---
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+ datasets: polymathic-ai/viscoelastic_instability
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+ tags:
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+ - physics
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+ ---
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+
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+ # Benchmarking Models on the Well
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+
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+ [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.
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+
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+
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+ 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.
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+ 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.
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+ We use the model performing best on the validation set to report test set results.
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+
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+ 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.
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+
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+ # CNextU-Net
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+
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+ Implementation of the [U-Net model](https://arxiv.org/abs/1505.04597) using [ConvNext blocks](https://arxiv.org/abs/2201.03545).
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+
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+ ## Model Details
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+
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+ For benchmarking on the Well, we used the following parameters.
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+
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+ | Parameters | Values |
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+ |---------------------|--------|
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+ | Spatial Filter Size | 7 |
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+ | Initial Dimension | 42 |
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+ | Block per Stage | 2 |
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+ | Up/Down Blocks | 4 |
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+ | Bottleneck Blocks | 1 |
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+
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+
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+ ## Trained Model Versions
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+
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+ Below is the list of checkpoints available for the training of CNextU-Net on different datasets of the Well.
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+
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+ | Dataset | Learning Rate | Epoch | VRMSE |
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+ |---------|---------------|-------|-------|
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+ | [acoustic_scattering_maze](https://huggingface.co/polymathic-ai/UNetConvNext-acoustic_scattering) | 1E-3 | 10 | 0.0196 |
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+ | [active_matter](https://huggingface.co/polymathic-ai/UNetConvNext-active_matter) | 5E-3 | 156 | 0.0953 |
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+ | [convective_envelope_rsg](https://huggingface.co/polymathic-ai/UNetConvNext-convective_envelope_rsg) | 1E-4 | 5 | 0.0663 |
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+ | [gray_scott_reaction_diffusion](https://huggingface.co/polymathic-ai/UNetConvNext-gray_scott_reaction_diffusion) | 1E-4 | 15 | 0.3596 |
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+ | [helmholtz_staircase](https://huggingface.co/polymathic-ai/UNetConvNext-helmholtz_staircase) | 5E-4 | 47 | 0.00146 |
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+ | [MHD_64](https://huggingface.co/polymathic-ai/UNetConvNext-MHD_64) | 5E-3 | 59 | 0.1487 |
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+ | [planetswe](https://huggingface.co/polymathic-ai/UNetConvNext-planetswe) | 1E-2 | 18 | 0.3268 |
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+ | [post_neutron_star_merger](https://huggingface.co/polymathic-ai/UNetConvNext-post_neutron_star_merger) | - | - | - |
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+ | [rayleigh_benard](https://huggingface.co/polymathic-ai/UNetConvNext-rayleigh_benard) | 5E-4 | 12 | 0.4807 |
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+ | [rayleigh_taylor_instability](https://huggingface.co/polymathic-ai/UNetConvNext-rayleigh_taylor_instability) | 5E-3 | 56 | 0.3771 |
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+ | [shear_flow](https://huggingface.co/polymathic-ai/UNetConvNext-shear_flow) | 5E-4 | 9 | 0.3972 |
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+ | [supernova_explosion_64](https://huggingface.co/polymathic-ai/UNetConvNext-supernova_explosion_64) | 5E-4 | 13 | 0.2801 |
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+ | [turbulence_gravity_cooling](https://huggingface.co/polymathic-ai/UNetConvNext-turbulence_gravity_cooling) | 1E-3 | 3 | 0.2093 |
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+ | [turbulent_radiative_layer_2D](https://huggingface.co/polymathic-ai/UNetConvNext-turbulent_radiative_layer_2D) | 5E-3 | 495 | 0.1247 |
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+ | [viscoelastic_instability](https://huggingface.co/polymathic-ai/UNetConvNext-viscoelastic_instability) | 5E-4 | 114 | 0.1966 |
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+
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+
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+ ## Loading the model from Hugging Face
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+
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+ To load the UNetConvNext model trained on the `viscoelastic_instability` of the Well, use the following commands.
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+
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+ ```python
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+ from the_well.benchmark.models import UNetConvNext
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+
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+ model = UNetConvNext.from_pretrained("polymathic-ai/UNetConvNext-viscoelastic_instability")
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+ ```
config.json ADDED
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+ {
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+ "blocks_at_neck": 1,
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+ "blocks_per_stage": 2,
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+ "dim_in": 32,
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+ "dim_out": 8,
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+ "gradient_checkpointing": false,
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+ "init_features": 42,
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+ "n_spatial_dims": 2,
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+ "spatial_resolution": [
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+ 512,
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+ 512
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+ ],
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+ "stages": 4
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+ }
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