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DTSTAMP:20250822T115805Z
LOCATION:Room 5.0B56
DTSTART;TZID=Europe/Stockholm:20250616T115000
DTEND;TZID=Europe/Stockholm:20250616T122000
UID:submissions.pasc-conference.org_PASC25_sess109_msa112@linklings.com
SUMMARY:Hybrid Modeling and Numerical Methods for Vlasov Equations
DESCRIPTION:emmanuel Franck (INRIA), Laurent Navoret (Unistra), Leo Bois a
 nd Victor Michel Dansac (INRIA), and Vincent Vigon (Unistra)\n\nPlasma phy
 sics simulations for fusion are particularly demanding in terms of computi
 ng time and memory consumption. Learning methods offer a way to reduce the
 se costs, but at the expense of losing theoretical guarantees and explaina
 bility. To mitigate these shortcomings, we propose exploring hybrid numeri
 cal methods and models that combine classical approaches on the one hand a
 nd learning-based terms on the other. We apply these approaches to acceler
 ate simulations of the Vlasov equation.\nWe will present reduced modeling 
 techniques for Vlasov-Poisson simulations in a weakly collisional regime, 
 where we use an Euler equations solver coupled with a neural network model
  that describes the non-equilibrium part of the kinetic distribution. Thro
 ugh examples, we will demonstrate the neural network model's ability to ca
 pture the missing term while also discussing the limitations of these appr
 oaches.\nFinally, we will briefly introduce new ideas for hybrid neural ne
 twork solvers and PIC codes to move toward more cost-effective simulations
 .\n\nDomain: Physics, Computational Methods and Applied Mathematics\n\nSes
 sion Chair: Laurent Villard (EPFL)\n\n
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