Push model using huggingface_hub.
Browse files- README.md +66 -0
- config.json +14 -0
- model.safetensors +3 -0
README.md
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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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# Benchmarking Models on the Well
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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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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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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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# CNextU-Net
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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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## Model Details
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For benchmarking on the Well, we used the following parameters.
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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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## Trained Model Versions
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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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| 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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## Loading the model from Hugging Face
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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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```python
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from the_well.benchmark.models import UNetConvNext
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model = UNetConvNext.from_pretrained("polymathic-ai/UNetConvNext-viscoelastic_instability")
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```
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config.json
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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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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:eafc75d1f852416b36646917b4f4b7c547038d657f9709924bf86b3c06583386
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size 74351024
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