BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Data-Driven Methods for Anomalous Resistivity in Collisionless Sho cks DTSTART:20251126T093000Z DTEND:20251126T110000Z DTSTAMP:20260506T071239Z UID:79ab7c16-064d-41c4-9d08-6cb672afb22b SEQUENCE:1 CREATED:20251121T095118Z DESCRIPTION: Collisionless shocks play a central role in plasma heating\, magnetic field amplification\, and nonthermal particle acceleration in bot h space and astrophysical plasmas. Their study\, however\, has long been c hallenged by its multi-scale nature\, where microscopic-scale kinetic proc esses can influence the large-scale dynamics of the system and vice-versa. Accurately capturing the nonlinear interplay between micro- and macro-sca le collisionless shock dynamics remains an outstanding problem. In this Th esis\, we explore the development of data-driven reduced models that can m ore efficiently capture the impact of microphysical instabilities on the l argescale shock dynamics. We perform first-principles particle-in-cell sim ulations of collisionless shocks and describe the corresponding electric f ield by a generalized Ohm’s law\, separating the contributions of transv ersely-averaged (mean-field) quantities and fluctuations due to transverse micro-instabilities. We then use convolutional neural networks to describ e the fluctuations in terms of mean fields\, closing the system. We demons trate the ability of this procedure to learn effective reduced models and reproduce the spatiotemporal profile of the anomalous electric field induc ed in a collisionless shock. We then explore network interrogation methods to help identify the physical terms most relevant to our network-based su rrogate anomalous field. This led to the identification of the mean quanti ties that control the dynamics of collisionless shocks and\, in the future \, could also inform the search for more interpretable reduced models. Our work opens the way to the incorporation of data-driven reduced models on fluid codes that could capture the effect of unresolved kinetic processes on the largescale collisionless shock dynamics. LAST-MODIFIED:20251121T095118Z LOCATION:Sala P3 (Piso 1 do Pavilhão de Matemática) do IST URL:http://df.vps.tecnico.ulisboa.pt/en/events/data-driven-methods-for-ano malous-resistivity-in-collisionless-shocks/ X-ALT-DESC;FMTTYPE=text/html:

Collisionless shoc ks play a central role in plasma heating\, magnetic field amplification\, and nonthermal particle acceleration in both space and astrophysical plasm as. Their study\, however\, has long been challenged by its multi-scale na ture\, where microscopic-scale kinetic processes can influence the large-s cale dynamics of the system and vice-versa. Accurately capturing the nonli near interplay between micro- and macro-scale collisionless shock dynamics remains an outstanding problem.

In this Thesis\, we explore the development of data-driven reduced models that can more efficiently captu re the impact of microphysical instabilities on the largescale shock dynam ics. We perform first-principles particle-in-cell simulations of collision less shocks and describe the corresponding electric field by a generalized Ohm’s law\, separating the contributions of transversely-averaged (mean -field) quantities and fluctuations due to transverse micro-instabilities. We then use convolutional neural networks to describe the fluctuations in terms of mean fields\, closing the system.

We demonstrate the a bility of this procedure to learn effective reduced models and reproduce t he spatiotemporal profile of the anomalous electric field induced in a col lisionless shock. We then explore network interrogation methods to help id entify the physical terms most relevant to our network-based surrogate ano malous field. This led to the identification of the mean quantities that c ontrol the dynamics of collisionless shocks and\, in the future\, could al so inform the search for more interpretable reduced models. Our work opens the way to the incorporation of data-driven reduced models on fluid codes that could capture the effect of unresolved kinetic processes on the larg escale collisionless shock dynamics.

END:VEVENT END:VCALENDAR