The Well: a Large-Scale Collection of Diverse Physics Simulations for
  Machine Learning
Machine learning based surrogate models offer researchers powerful tools for
accelerating simulation-based workflows. However, as standard datasets in this
space often cover small classes of physical behavior, it can be difficult to
evaluate the efficacy of new approaches. To address this gap, we introduce the
Well: a large-scale collection of datasets containing numerical simulations of
a wide variety of spatiotemporal physical systems. The Well draws from domain
experts and numerical software developers to provide 15TB of data across 16
datasets covering diverse domains such as biological systems, fluid dynamics,
acoustic scattering, as well as magneto-hydrodynamic simulations of
extra-galactic fluids or supernova explosions. These datasets can be used
individually or as part of a broader benchmark suite. To facilitate usage of
the Well, we provide a unified PyTorch interface for training and evaluating
models. We demonstrate the function of this library by introducing example
baselines that highlight the new challenges posed by the complex dynamics of
the Well. The code and data is available at
https://github.com/PolymathicAI/the_well.
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Ruben Ohana
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2024-12-03T18:43:11.357Z
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Ruben Ohana
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Michael McCabe
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Lucas Meyer
claimed_verified
2025-03-20T10:55:01.527Z
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Lucas Meyer
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LTMeyer
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Rudy Morel
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Fruzsina J. Agocs
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Miguel Beneitez
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2025-05-08T09:00:25.084Z
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Miguel Beneitez
user
mikibene
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Marsha Berger
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Blakesley Burkhart
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Stuart B. Dalziel
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Drummond B. Fielding
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Daniel Fortunato
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Jared A. Goldberg
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Keiya Hirashima
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Yan-Fei Jiang
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Rich R. Kerswell
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Suryanarayana Maddu
674f356ea03fd00bab16cdd2
Jonah Miller
674f356ea03fd00bab16cdd3
Payel Mukhopadhyay
674f356ea03fd00bab16cdd4
Stefan S. Nixon
674f356ea03fd00bab16cdd5
Jeff Shen
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Romain Watteaux
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Bruno Régaldo-Saint Blancard
claimed_verified
2024-12-03T18:43:09.531Z
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Bruno Regaldo
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bregaldo
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François Rozet
claimed_verified
2025-07-11T13:13:26.566Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65a54541974f8f811f722741/EAeK9QDTheyQOHw_euP34.jpeg
François Rozet
user
francois-rozet
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Liam H. Parker
674f356ea03fd00bab16cdda
Miles Cranmer
674f356ea03fd00bab16cddb
Shirley Ho
The Well: a Large-Scale Collection of Diverse Physics Simulations for
  Machine Learning
Machine learning based surrogate models offer researchers powerful tools for
accelerating simulation-based workflows. However, as standard datasets in this
space often cover small classes of physical behavior, it can be difficult to
evaluate the efficacy of new approaches. To address this gap, we introduce the
Well: a large-scale collection of datasets containing numerical simulations of
a wide variety of spatiotemporal physical systems. The Well draws from domain
experts and numerical software developers to provide 15TB of data across 16
datasets covering diverse domains such as biological systems, fluid dynamics,
acoustic scattering, as well as magneto-hydrodynamic simulations of
extra-galactic fluids or supernova explosions. These datasets can be used
individually or as part of a broader benchmark suite. To facilitate usage of
the Well, we provide a unified PyTorch interface for training and evaluating
models. We demonstrate the function of this library by introducing example
baselines that highlight the new challenges posed by the complex dynamics of
the Well. The code and data is available at
https://github.com/PolymathicAI/the_well.
A large-scale dataset collection, The Well, provides diverse numerical simulations for benchmarking machine learning models in physical systems simulation.
surrogate models simulation-based workflows numerical simulations spatiotemporal physical systems biological systems fluid dynamics acoustic scattering magneto-hydrodynamic simulations PyTorch interface benchmark suite