{"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/towards-large-scale-learned-solvers-for","title":"Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs","arxiv_id":"2204.01205","date":"2022-04-04","proceeding":null,"authors":["Thomas J. Grady II","Rishi Khan","Mathias Louboutin","Ziyi Yin","Philipp A. Witte","Ranveer Chandra","Russell J. Hewett","Felix J. Herrmann"],"abstract":"Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches. Once trained, FNOs can achieve speed-ups of multiple orders of magnitude over conventional numerical PDE solvers. However, due to the high dimensionality of their input data and network weights, FNOs have so far only been applied to two-dimensional or small three-dimensional problems. To remove this limited problem-size barrier, we propose a model-parallel version of FNOs based on domain-decomposition of both the input data and network weights. 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