{"ID":2863109,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00282","arxiv_id":"2510.00282","title":"Electron neural closure for turbulent magnetosheath simulations: energy channels","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.","short_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 kin...","url_abs":"https://arxiv.org/abs/2510.00282","url_pdf":"https://arxiv.org/pdf/2510.00282v2","authors":"[\"George Miloshevich\",\"Luka Vranckx\",\"Felipe Nathan de Oliveira Lopes\",\"Pietro Dazzi\",\"Giuseppe Arrò\",\"Giovanni Lapenta\"]","published":"2025-09-30T21:00:50Z","proceeding":"physics.plasm-ph","tasks":"[\"physics.plasm-ph\",\"cs.LG\",\"physics.comp-ph\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}