Magnetohydrodynamics with Physics Informed Neural Operators

• URL: http://arxiv.org/abs/2302.08332v2
• Date: Fri, 7 Jul 2023 18:04:10 GMT
• Title: Magnetohydrodynamics with Physics Informed Neural Operators
• Authors: Shawn G. Rosofsky and E. A. Huerta
• Abstract summary: We explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of methods. We present the first application of physics informed neural operators to model 2D incompressible magnetohydrodynamics simulations.
• Score: 2.588973722689844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 to model 2D incompressible magnetohydrodynamics simulations. Our AI models incorporate tensor Fourier neural operators as their backbone, which we implemented with the TensorLY package. Our results indicate that physics informed neural operators can accurately capture the physics of magnetohydrodynamics simulations that describe laminar flows with Reynolds numbers $Re\leq250$. 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 magnetohydrodynamics simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript.

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