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.
arXiv comment19 pages, 10 figures, 4 tables
References in corpus (37)
- Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
- Enhancing Computational Fluid Dynamics with Machine Learning
- Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
- Neural General Circulation Models for Weather and Climate
- The Energy Conserving Particle-in-Cell Method
- 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
- Vlasov methods in space physics and astrophysics
- Quantifying Gyrotropy in Magnetic Reconnection
- DeepMoD: Deep learning for Model Discovery in noisy data
- Exactly Energy Conserving Semi-Implicit Particle in Cell Formulation
- Turbulent Reconnection and Its Implications
- Comparison of multi-fluid moment models with Particle-in-Cell simulations of collisionless magnetic reconnection
- Global Ten-Moment Multifluid Simulations of the Solar Wind Interaction with Mercury: From the Planetary Conducting Core to the Dynamic Magnetosphere
- Data-driven discovery of reduced plasma physics models from fully-kinetic simulations
- A posteriori learning for quasi-geostrophic turbulence parametrization
- Hamiltonian Formalism of Extended Magnetohydrodynamics
- An introductory guide to fluid models with anisotropic temperatures Part 1 -- CGL description and collisionless fluid hierarchy
- Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
- Concomitant Hamiltonian and topological structures of extended magnetohydrodynamics
- Machine Learning Surrogate Models for Landau Fluid Closure
- An introductory guide to fluid models with anisotropic temperatures Part 2 -- Kinetic theory, Padé approximants and Landau fluid closures
- 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
- Neural network representability of fully ionized plasma fluid model closures
- 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
- Inverse cascade and magnetic vortices in kinetic Alfvén-wave turbulence
- Identification of high order closure terms from fully kinetic simulations using machine learning
- Numerical Study of Magnetic Island Coalescence Using Magnetohydrodynamics With Adaptively Embedded Particle-In-Cell Model
- Modeling imbalanced collisionless Alfvén wave turbulence with nonlinear diffusion equations
- The muphyII Code: Multiphysics Plasma Simulation on Large HPC Systems
- 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
- Hyperbolic Machine Learning Moment Closures for the BGK Equations