title: The Well: a Large-Scale Collection of Diverse Physics Simulations for
    Machine Learning
summary: 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.
paper_authors._id: 674f356ea03fd00bab16cdc2
paper_authors.name: Ruben Ohana
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-03T18:43:11.357Z
paper_authors._id: 630495adeb6d777a838d27b4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/630495adeb6d777a838d27b4/qCtQ8t37vYDOsQukDznTZ.jpeg
paper_authors.fullname: Ruben Ohana
paper_authors.type: user
paper_authors.user: rubenohana
paper_authors._id: 674f356ea03fd00bab16cdc3
paper_authors.name: Michael McCabe
paper_authors._id: 674f356ea03fd00bab16cdc4
paper_authors.name: Lucas Meyer
paper_authors._id: 674f356ea03fd00bab16cdc5
paper_authors.name: Rudy Morel
paper_authors._id: 674f356ea03fd00bab16cdc6
paper_authors.name: Fruzsina J. Agocs
paper_authors._id: 674f356ea03fd00bab16cdc7
paper_authors.name: Miguel Beneitez
paper_authors._id: 674f356ea03fd00bab16cdc8
paper_authors.name: Marsha Berger
paper_authors._id: 674f356ea03fd00bab16cdc9
paper_authors.name: Blakesley Burkhart
paper_authors._id: 674f356ea03fd00bab16cdca
paper_authors.name: Stuart B. Dalziel
paper_authors._id: 674f356ea03fd00bab16cdcb
paper_authors.name: Drummond B. Fielding
paper_authors._id: 674f356ea03fd00bab16cdcc
paper_authors.name: Daniel Fortunato
paper_authors._id: 674f356ea03fd00bab16cdcd
paper_authors.name: Jared A. Goldberg
paper_authors._id: 674f356ea03fd00bab16cdce
paper_authors.name: Keiya Hirashima
paper_authors._id: 674f356ea03fd00bab16cdcf
paper_authors.name: Yan-Fei Jiang
paper_authors._id: 674f356ea03fd00bab16cdd0
paper_authors.name: Rich R. Kerswell
paper_authors._id: 674f356ea03fd00bab16cdd1
paper_authors.name: Suryanarayana Maddu
paper_authors._id: 674f356ea03fd00bab16cdd2
paper_authors.name: Jonah Miller
paper_authors._id: 674f356ea03fd00bab16cdd3
paper_authors.name: Payel Mukhopadhyay
paper_authors._id: 674f356ea03fd00bab16cdd4
paper_authors.name: Stefan S. Nixon
paper_authors._id: 674f356ea03fd00bab16cdd5
paper_authors.name: Jeff Shen
paper_authors._id: 674f356ea03fd00bab16cdd6
paper_authors.name: Romain Watteaux
paper_authors._id: 674f356ea03fd00bab16cdd7
paper_authors.name: Bruno Régaldo-Saint Blancard
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-03T18:43:09.531Z
paper_authors._id: 636fe3736cd69d9a36053644
paper_authors.avatarUrl: /avatars/3d43af3a63669ab78d9c15d091f0c6ce.svg
paper_authors.fullname: Bruno Regaldo
paper_authors.type: user
paper_authors.user: bregaldo
paper_authors._id: 674f356ea03fd00bab16cdd8
paper_authors.name: François Rozet
paper_authors._id: 674f356ea03fd00bab16cdd9
paper_authors.name: Liam H. Parker
paper_authors._id: 674f356ea03fd00bab16cdda
paper_authors.name: Miles Cranmer
paper_authors._id: 674f356ea03fd00bab16cddb
paper_authors.name: Shirley Ho
paper_title: The Well: a Large-Scale Collection of Diverse Physics Simulations for
    Machine Learning
paper_summary: 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.
paper_ai_keywords: PyTorch biological systems fluid dynamics acoustic scattering magneto-hydrodynamic simulations supernova explosions