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:20260506T041947Z UID:79ab7c16-064d-41c4-9d08-6cb672afb22b SEQUENCE:2 CREATED:20251121T095125Z DESCRIPTION:Collisionless shocks play a central role in plasma heating\, m agnetic field amplification\, and nonthermal particle acceleration in both space and astrophysical plasmas. Their study\, however\, has long been ch allenged by its multi-scale nature\, where microscopic-scale kinetic proce sses can influence the large-scale dynamics of the system and vice-versa. Accurately capturing the nonlinear interplay between micro- and macro-scal e collisionless shock dynamics remains an outstanding problem. In this The sis\, we explore the development of data-driven reduced models that can mo re efficiently capture the impact of microphysical instabilities on the la rgescale shock dynamics. We perform first-principles particle-in-cell simu lations of collisionless shocks and describe the corresponding electric fi eld by a generalized Ohm’s law\, separating the contributions of transve rsely-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 demonst rate the ability of this procedure to learn effective reduced models and r eproduce the spatiotemporal profile of the anomalous electric field induce d in a collisionless shock. We then explore network interrogation methods to help identify the physical terms most relevant to our network-based sur rogate anomalous field. This led to the identification of the mean quantit ies 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 f luid codes that could capture the effect of unresolved kinetic processes o n the largescale collisionless shock dynamics. LAST-MODIFIED:20251121T095136Z LOCATION:Sala P3 (Piso 1 do Pavilhão de Matemática) do IST URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/data-driven-methods-for-an omalous-resistivity-in-collisionless-shocks/ X-ALT-DESC;FMTTYPE=text/html:

Collisionless shock s play a central role in plasma heating\, magnetic field amplification\, a nd nonthermal particle acceleration in both space and astrophysical plasma s. Their study\, however\, has long been challenged by its multi-scale nat ure\, where microscopic-scale kinetic processes can influence the large-sc ale dynamics of the system and vice-versa. Accurately capturing the nonlin ear 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 captur e the impact of microphysical instabilities on the largescale shock dynami cs. We perform first-principles particle-in-cell simulations of collisionl ess 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 ab ility of this procedure to learn effective reduced models and reproduce th e spatiotemporal profile of the anomalous electric field induced in a coll isionless shock. We then explore network interrogation methods to help ide ntify the physical terms most relevant to our network-based surrogate anom alous field. This led to the identification of the mean quantities that co ntrol the dynamics of collisionless shocks and\, in the future\, could als o 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 large scale collisionless shock dynamics.

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