  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.
