000007937 001__ 7937
000007937 005__ 20260211173550.0
000007937 0247_ $$2DOI$$a10.1063/5.0300009
000007937 037__ $$aSCART-2026-0099
000007937 100__ $$aMiloshevich, George
000007937 245__ $$aElectron neural closure for turbulent magnetosheath simulations: Energy channels
000007937 260__ $$c2026
000007937 520__ $$aIn this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a fully convolutional neural network (FCNN). Electron pressure plays an important role in generalized Ohm's law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit particle-in-cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with a smaller number of particles per cell and showing that our results generalize to a simulation with a large number of particles per cell. We evaluate the statistical properties of the learned equation of state, with a focus on pressure-strain interaction, which is crucial for understanding energy channels in turbulent plasmas. The resulting equation of state learned via FCNN significantly outperforms local closures, such as those learned by multi-layer perceptron (MLP) or double adiabatic expressions. We report that the overall spatial distribution of pressure-strain and its conditional averages are reconstructed well. However, some small-scale features are missed, especially for the off-diagonal components of the pressure tensor. Nevertheless, the results are substantially improved with more training data, indicating favorable scaling and potential for improvement, which will be addressed in future work.
000007937 594__ $$aNO
000007937 700__ $$aVranckx, Luka
000007937 700__ $$ade Oliveira Lopes, Felipe Nathan
000007937 700__ $$aDazzi, Pietro
000007937 700__ $$aArrò, Giuseppe
000007937 700__ $$aLapenta, Giovanni
000007937 773__ $$n1$$pPhysics of Plasmas$$v33$$y2026
000007937 8560_ $$fluka.vranckx@ksb-orb.be
000007937 85642 $$ahttps://pubs.aip.org/aip/pop/article/33/1/012901/3377334/Electron-neural-closure-for-turbulent
000007937 8564_ $$s11346045$$uhttps://publi2-as.oma.be/record/7937/files/Miloshevich et al. 2026 - Electron neural closure for turbulent magnetosheath simulations Energy channels.pdf
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000007937 905__ $$apublished in
000007937 980__ $$aREFERD