  Studying the sun's outer atmosphere is challenging due to its complex
magnetic fields impacting solar activities. Magnetohydrodynamics (MHD)
simulations help model these interactions but are extremely time-consuming
(usually on a scale of days). Our research applies the Fourier Neural Operator
(FNO) to accelerate the coronal magnetic field modeling, specifically, the
Bifrost MHD model. We apply Tensorized FNO (TFNO) to generate solutions from
partial differential equations (PDEs) over a 3D domain efficiently. TFNO's
performance is compared with other deep learning methods, highlighting its
accuracy and scalability. Physics analysis confirms that TFNO is reliable and
capable of accelerating MHD simulations with high precision. This advancement
improves efficiency in data handling, enhances predictive capabilities, and
provides a better understanding of magnetic topologies.
