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 atthis https URL.

@article{ohana2025_2412.00568,
  title={ The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning },
  author={ Ruben Ohana and Michael McCabe and Lucas Meyer and Rudy Morel and Fruzsina J. Agocs and Miguel Beneitez and Marsha Berger and Blakesley Burkhart and Keaton Burns and Stuart B. Dalziel and Drummond B. Fielding and Daniel Fortunato and Jared A. Goldberg and Keiya Hirashima and Yan-Fei Jiang and Rich R. Kerswell and Suryanarayana Maddu and Jonah Miller and Payel Mukhopadhyay and Stefan S. Nixon and Jeff Shen and Romain Watteaux and Bruno Régaldo-Saint Blancard and François Rozet and Liam H. Parker and Miles Cranmer and Shirley Ho },
  journal={arXiv preprint arXiv:2412.00568},
  year={ 2025 }
}