ML Anthology

The Well: A Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Ruben Ohana, Michael McCabe, Lucas Thibaut Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond Buschman Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich Kerswell, Suryanarayana Maddu, Jonah M. Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Miles Cranmer, Shirley Ho
NeurIPSW 2024
/neuripsw/2024/ohana2024neuripsw-well/

Abstract

Machine learning (ML) 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 \emph{the Well:} a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain scientists 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 poses by the complex dynamics of the Well.

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Cite

Text

Ohana et al. "The Well: A Large-Scale Collection of Diverse Physics Simulations for Machine Learning." NeurIPS 2024 Workshops: D3S3, 2024.

Markdown

[Ohana et al. "The Well: A Large-Scale Collection of Diverse Physics Simulations for Machine Learning." NeurIPS 2024 Workshops: D3S3, 2024.](https://mlanthology.org/neuripsw/2024/ohana2024neuripsw-well/)

BibTeX

@inproceedings{ohana2024neuripsw-well,
  title     = {{The Well: A Large-Scale Collection of Diverse Physics Simulations for Machine Learning}},
  author    = {Ohana, Ruben and McCabe, Michael and Meyer, Lucas Thibaut and Morel, Rudy and Agocs, Fruzsina Julia and Beneitez, Miguel and Berger, Marsha and Burkhart, Blakesley and Dalziel, Stuart B. and Fielding, Drummond Buschman and Fortunato, Daniel and Goldberg, Jared A. and Hirashima, Keiya and Jiang, Yan-Fei and Kerswell, Rich and Maddu, Suryanarayana and Miller, Jonah M. and Mukhopadhyay, Payel and Nixon, Stefan S. and Shen, Jeff and Watteaux, Romain and Blancard, Bruno Régaldo-Saint and Parker, Liam Holden and Cranmer, Miles and Ho, Shirley},
  booktitle = {NeurIPS 2024 Workshops: D3S3},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/ohana2024neuripsw-well/}
}