Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field Model

TL;DR

This paper introduces a novel global-local Fourier Neural Operator (GL-FNO) that significantly accelerates coronal magnetic field simulations, achieving over 20,000 times speedup while maintaining high accuracy and reliability, thus advancing space weather research.

Contribution

The paper proposes the first global-local FNO architecture for coronal magnetic field modeling, combining global and local features for improved accuracy and efficiency in MHD simulations.

Findings

GL-FNO achieves over 20,000x speedup in simulation time.

GL-FNO outperforms existing deep learning models in accuracy and scalability.

Physics-based validation confirms the reliability of GL-FNO predictions.

Abstract

Exploring the outer atmosphere of the sun has remained a significant bottleneck in astrophysics, given the intricate magnetic formations that significantly influence diverse solar events. Magnetohydrodynamics (MHD) simulations allow us to model the complex interactions between the sun's plasma, magnetic fields, and the surrounding environment. However, MHD simulation is extremely time-consuming, taking days or weeks for simulation. The goal of this study is to accelerate coronal magnetic field simulation using deep learning, specifically, the Fourier Neural Operator (FNO). FNO has been proven to be an ideal tool for scientific computing and discovery in the literature. In this paper, we proposed a global-local Fourier Neural Operator (GL-FNO) that contains two branches of FNOs: the global FNO branch takes downsampled input to reconstruct global features while the local FNO branch takes original resolution input to capture fine details. The performance of the GLFNO is compared with state-of-the-art deep learning methods, including FNO, U-NO, U-FNO, Vision Transformer, CNN-RNN, and CNN-LSTM, to demonstrate its accuracy, computational efficiency, and scalability. Furthermore, physics analysis from domain experts is also performed to demonstrate the reliability of GL-FNO. The results demonstrate that GL-FNO not only accelerates the MHD simulation (a few seconds for prediction, more than \times 20,000 speed up) but also provides reliable prediction capabilities, thus greatly contributing to the understanding of space weather dynamics. Our code implementation is available at https://github.com/Yutao-0718/GL-FNO