Checkpoints of the models benchmarked on the Well in the original paper.
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We advance Science through Multi‑Disciplinary AI
We usher in a new class of machine learning for scientific data, building models that can leverage shared concepts across disciplines. We aim to develop, train, and release such foundation models for use by researchers worldwide.
Collections
2
models
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polymathic-ai/UNetClassic-viscoelastic_instability
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polymathic-ai/UNetClassic-turbulence_gravity_cooling
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polymathic-ai/UNetClassic-supernova_explosion_64
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polymathic-ai/UNetClassic-shear_flow
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polymathic-ai/UNetClassic-rayleigh_taylor_instability
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polymathic-ai/UNetClassic-rayleigh_benard
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polymathic-ai/UNetClassic-planetswe
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polymathic-ai/UNetClassic-MHD_64
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polymathic-ai/UNetClassic-helmholtz_staircase
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polymathic-ai/UNetClassic-gray_scott_reaction_diffusion
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datasets
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polymathic-ai/viscoelastic_instability
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polymathic-ai/turbulent_radiative_layer_2D
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2
polymathic-ai/turbulence_gravity_cooling
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255
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1
polymathic-ai/supernova_explosion_64
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polymathic-ai/shear_flow
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1
polymathic-ai/rayleigh_taylor_instability
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polymathic-ai/rayleigh_benard
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polymathic-ai/post_neutron_star_merger
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139
polymathic-ai/planetswe
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93
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polymathic-ai/helmholtz_staircase
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432