Neural Operators Learn the Local Physics of Magnetohydrodynamics

• URL: http://arxiv.org/abs/2404.16015v2
• Date: Thu, 10 Oct 2024 14:19:57 GMT
• Title: Neural Operators Learn the Local Physics of Magnetohydrodynamics
• Authors: Taeyoung Kim, Youngsoo Ha, Myungjoo Kang,
• Abstract summary: Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids. Recent advances introduce neural operators like the Fourier Neural Operator (FNO) as surrogate models for traditional numerical analyses. This study explores a modified Flux Fourier neural operator model to approximate the numerical flux of ideal MHD.
• Score: 6.618373975988337
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• Abstract: Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids, essential for understanding phenomena such as the structure and evolution of stars and galaxies, and in nuclear fusion for plasma motion through ideal MHD equations. Solving these hyperbolic PDEs requires sophisticated numerical methods, presenting computational challenges due to complex structures and high costs. Recent advances introduce neural operators like the Fourier Neural Operator (FNO) as surrogate models for traditional numerical analyses. This study explores a modified Flux Fourier neural operator model to approximate the numerical flux of ideal MHD, offering a novel approach that outperforms existing neural operator models by enabling continuous inference, generalization outside sampled distributions, and faster computation compared to classical numerical schemes.

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