{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-well-a-large-scale-collection-of-diverse","title":"The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning","arxiv_id":"2412.00568","date":"2024-11-30","proceeding":null,"authors":["Ruben Ohana","Michael McCabe","Lucas Meyer","Rudy Morel","Fruzsina J. Agocs","Miguel Beneitez","Marsha Berger","Blakesley Burkhart","Keaton Burns","Stuart B. Dalziel","Drummond B. Fielding","Daniel Fortunato","Jared A. Goldberg","Keiya Hirashima","Yan-Fei Jiang","Rich R. Kerswell","Suryanarayana Maddu","Jonah Miller","Payel Mukhopadhyay","Stefan S. Nixon","Jeff Shen","Romain Watteaux","Bruno Régaldo-Saint Blancard","François Rozet","Liam H. Parker","Miles Cranmer","Shirley Ho"],"abstract":"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. 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