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Title: Magnetohydrodynamics with physics informed neural operators
Abstract

The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators (NOs) (PINOs) to model 2D incompressible magnetohydrodynamics (MHD) simulations. Our AI models incorporate tensor Fourier NOs as their backbone, which we implemented with theTensorLYpackage. Our results indicate that PINOs can accurately capture the physics of MHD simulations that describe laminar flows with Reynolds numbersRe250. We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of MHD simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript.

 
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Award ID(s):
1931561
NSF-PAR ID:
10429345
Author(s) / Creator(s):
;
Publisher / Repository:
IOP Publishing
Date Published:
Journal Name:
Machine Learning: Science and Technology
Volume:
4
Issue:
3
ISSN:
2632-2153
Page Range / eLocation ID:
Article No. 035002
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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