Data-Driven Closures for Hybrid Plasma Models in Space Plasmas - George Miloshevich

Abstract: Modelling turbulence kinetically in space remains challenging due to the multiscale nature of plasma. One approach is to adopt a fluid model hierarchy and close it using a phenomenological expression or law derived from local kinetic simulations. We address this challenge from the perspective of decaying turbulence in the near-Earth magnetosheath using fully kinetic particle-in-cell (PIC) simulations. We apply machine learning techniques to extract a non-local five-moment electron-pressure-tensor closure trained on these simulations. We evaluate the learned “equation of state” using energy-channel diagnostics, emphasizing the pressure–strain interaction (a key mediator of turbulence heating). The new global closure outperforms common local approaches (e.g., double-adiabatic and MLP-type closures) in reconstructing key statistics. Equation of state trained on simulations with fewer particles per cell generalises to more accurate simulations with a high number of particles per cell and different turbulent initialization, but with the same physical parameters. We couple this data-driven electron closure with kinetic ion dynamics, advancing toward hybrid kinetic simulations in which electrons are represented by a neural network-based equation of state.