The Well: a Large-Scale Collection of Diverse Physics Simulations for
  Machine Learning
Authors: name: Ruben Ohana, name: Michael McCabe, name: Lucas Meyer, name: Rudy Morel, name: Fruzsina J. Agocs, name: Miguel Beneitez, name: Marsha Berger, name: Blakesley Burkhart, name: Stuart B. Dalziel, name: Drummond B. Fielding, name: Daniel Fortunato, name: Jared A. Goldberg, name: Keiya Hirashima, name: Yan-Fei Jiang, name: Rich R. Kerswell, name: Suryanarayana Maddu, name: Jonah Miller, name: Payel Mukhopadhyay, name: Stefan S. Nixon, name: Jeff Shen, name: Romain Watteaux, name: Bruno Régaldo-Saint Blancard, name: François Rozet, name: Liam H. Parker, name: Miles Cranmer, name: Shirley Ho

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.