Electron neural closure for turbulent magnetosheath simulations: energy channels
arXiv:2510.00282 · doi:10.1063/5.0300009
Abstract
In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a Fully Convolutional Neural Network (FCNN). Electron pressure plays an important role in generalized Ohm's law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit Particle-in-Cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with a smaller number of particles per cell and showing that our results generalise to a simulation with a large number of particles per cell. We evaluate the statistical properties of the learned equation of state, with a focus on pressure-strain interaction, which is crucial for understanding energy channels in turbulent plasmas. The resulting equation of state learned via FCNN significantly outperforms local closures, such as those learned by Multi-Layer Perceptron (MLP) or double adiabatic expressions. We report that the overall spatial distribution of pressure-strain and its conditional averages are reconstructed well. However, some small-scale features are missed, especially for the off-diagonal components of the pressure tensor. Nevertheless, the results are substantially improved with more training data, indicating favorable scaling and potential for improvement, which will be addressed in future work.
19 pages, 10 figures, 4 tables
References in corpus (20)
- Enhancing Computational Fluid Dynamics with Machine Learning
- Neural General Circulation Models for Weather and Climate
- One-to-one direct modeling of experiments and astrophysical scenarios: pushing the envelope on kinetic plasma simulations
- 3D Turbulent Reconnection: Theory, Tests and Astrophysical Implications
- A posteriori learning for quasi-geostrophic turbulence parametrization
- Hamiltonian Formalism of Extended Magnetohydrodynamics
- Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
- Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
- Spectral properties and energy transfer at kinetic scales in collisionless plasma turbulence
- Bridging hybrid- and full-kinetic models with Landau-fluid electrons: I. 2D magnetic reconnection
- Do we need to consider electron kinetic effects to properly model a planetary magnetosphere: the case of Mercury
- Machine-learning heat flux closure for multi-moment fluid modeling of nonlinear Landau damping
- In Search of a Data Driven Symbolic Multi-Fluid 10-Moment Model Closure
- Modeling imbalanced collisionless Alfvén wave turbulence with nonlinear diffusion equations
- Numerical Study of Magnetic Island Coalescence Using Magnetohydrodynamics With Adaptively Embedded Particle-In-Cell Model
- Identification of high order closure terms from fully kinetic simulations using machine learning
- Data-driven discovery of a heat flux closure for electrostatic plasma phenomena
- Menura: a code for simulating the interaction between a turbulent solar wind and solar system bodies
- The muphyII Code: Multiphysics Plasma Simulation on Large HPC Systems
- Hyperbolic Machine Learning Moment Closures for the BGK Equations