arXiv:2510.00282v2 Announce Type: replace-cross 
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.

تقدم دراسة جديدة إغلاقًا لمتجه ضغط الإلكترون غير المحلي من خمسة لحظات، باستخدام شبكة عصبية تلافيفية كاملة (FCNN) لتحسين محاكاة المغناطيسية المضطربة. يهدف هذا النموذج إلى زيادة دقة المحاكاة شبه الضمنية للجزئيات التي تحافظ على الطاقة من خلال التدريب على مجموعة تمثيلية من المحاكاة ذات كثافات جزئية متغيرة.

Un nuevo estudio presenta un cierre de tensor de presión electrónica de cinco momentos no local, utilizando una Red Neuronal Convolucional Total (FCNN) para mejorar las simulaciones turbulentas del magnetosheath. Este modelo tiene como objetivo aumentar la precisión de las simulaciones semi-implícitas de partículas en celda que conservan energía, entrenándose en un conjunto representativo de simulaciones con diferentes densidades de partículas.

Une nouvelle étude présente une fermeture de tenseur de pression électronique à cinq moments non locale, utilisant un Réseau de Neurones Convolutifs Entiers (FCNN) pour améliorer les simulations turbulentes de la magnétopause. Ce modèle vise à accroître la précision des simulations semi-implicites de particules en cellule conservant l'énergie en s'entraînant sur un ensemble représentatif de simulations avec des densités de particules variées.

A new study introduces a non-local five-moment electron pressure tensor closure, utilizing a Fully Convolutional Neural Network (FCNN) to enhance turbulent magnetosheath simulations. This model aims to improve the accuracy of energy-conserving semi-implicit Particle-in-Cell simulations by training on a representative set of simulations with varying particle densities.

Electron neural closure for turbulent magnetosheath simulations: energy channels

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