BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Frederico Fiuza (SLAC) DTSTART:20221103T170000Z DTEND:20221103T180000Z DTSTAMP:20260423T003251Z UID:MPML/86 DESCRIPTION:Title: Ac celerating the understanding of nonlinear dynamical systems using machine learning\nby Frederico Fiuza (SLAC) as part of Mathematics\, Physics a nd Machine Learning (IST\, Lisbon)\n\n\nAbstract\nThe description of nonli near\, multi-scale dynamics is a common challenge in a wide range of physi cal systems and research fields — from weather forecast to controlled nu clear fusion. The development of reduced models that balance between accur acy and complexity is critical to advancing theoretical comprehension and enabling holistic computational descriptions of these problems. I will dis cuss how techniques from statistical and machine learning are offering new ways of inferring reduced physics models from the increasingly abundant d ata of nonlinear dynamics produced by experiments\, observations\, and sim ulations. In particular\, I will focus on how sparse regression techniques can be used to infer interpretable plasma physics models (in the form of nonlinear partial differential equations) directly from the data of first- principles fully-kinetic simulations. The potential of this approach is de monstrated by recovering the fundamental hierarchy of plasma physics model s based solely on particle-based simulation data of complex plasma dynamic s. I will discuss how this data-driven methodology provides a promising to ol to accelerate the development of reduced theoretical models of nonlinea r dynamical systems and to design computationally efficient algorithms for multi-scale simulations.\n LOCATION:https://researchseminars.org/talk/MPML/86/ END:VEVENT END:VCALENDAR