Numerical Modeling of Plasmas Across Fluid and Kinetic Regimes

Plasmas exhibit a wide range of behaviors across fluid and kinetic regimes, governing key processes in diverse laboratory, space, and astrophysical environments. Understanding these systems requires numerical models with varying levels of physical fidelity, including magnetohydrodynamic (MHD), multi-moment multifluid, and particle-in-cell (PIC) approaches, as well as combined methods such as hybrid-PIC and adaptively embedded particle-in-cell (MHD-AEPIC) models. These approaches enable us to capture multiscale dynamics and a broad range of plasma processes.

In this talk, I will present several examples that connect plasma dynamics across scales using different modeling approaches, including local simulations of magnetic reconnection and turbulence, as well as global studies of star–planet interactions in our solar system and beyond. I will also demonstrate how modern machine learning techniques can be leveraged to incorporate kinetic physics into fluid models, enabling them to reproduce kinetic simulation results without explicitly resolving phase-space dynamics.