1,1,1,2,- 1KU Leuven, CmPA, Mathematics, Leuven, Belgium (george.miloshevich@kuleuven.be)
- 2Department of Physics, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA a)
- 3Laboratoire Lagrange, Observatoire de la Côte d’Azur, Université Côte d’Azur (OCA), CNRS, Nice, France
Modelling turbulence kinetically in space remains challenging due to the multiscale nature of plasma. An alternative 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 by examining decaying turbulence in the near-Earth magnetosheath using fully kinetic particle-in-cell (PIC) simulations [1]. We apply machine learning techniques to extract a non-local five-moment electron-pressure-tensor closure trained on these simulations. The data are carefully split across simulations initialized with different initial conditions, while maintaining the same turbulence and temperature levels. We evaluate the learned “equation of state” using energy-channel diagnostics, with emphasis on the pressure–strain interaction (a key mediator of turbulence heating). The new global closure outperforms common local approaches (e.g., double-adiabatic [2] and MLP-type closures [3]) in reconstructing key statistics. An equation of state trained on simulations with fewer particles per cell generalises to more accurate simulations with a higher number of particles per cell and different turbulent initialisations, while using the same physical parameters. Off-diagonal terms are more challenging to predict, but performance improves with the quantity of training data.
Finally, 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. This hybrid physics-informed machine learning framework offers a pathway to computationally efficient models with improved physical realism, potentially enabling both predictive simulations and parameter inference in heliospheric and magnetospheric applications.
How to cite: Miloshevich, G., Vranckx, L., de Oliveira Lopes, F. N., Dazzi, P., Arrò, G., and Henri, P.: Electron Neural Closure for Turbulent Magnetosheath Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12428, https://doi.org/10.5194/egusphere-egu26-12428, 2026.
