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DTSTART;TZID=Europe/Stockholm:20240604T110000
DTEND;TZID=Europe/Stockholm:20240604T130000
UID:submissions.pasc-conference.org_PASC24_sess116@linklings.com
SUMMARY:MS3G - High Performance Computing for Magnetic Fusion Applications
  - Part I
DESCRIPTION:Minisymposium\n\nThis series of three minisymposia will be ded
 icated to addressing frontier challenges in magnetic fusion research. (1) 
 Machine Learning and Quantum Computing: the four speakers will cover vario
 us aspects of machine learning, from real-time control of tokamaks to turb
 ulence simulations to HPC issues. One talk will be devoted to the topic of
  quantum computing and examine opportunities for application in the field 
 of fusion plasma physics. (2) New developments for Edge and Scrape-Off Lay
 er (SOL) simulations: this is recognized as a frontier domain, involving s
 ignificant challenges at various levels. Three talks will be devoted to pr
 ogress made on three different kinetic codes, while a generalization of gy
 rokinetic models to magnetized sheath conditions will be presented in a fo
 urth talk. (3) Beyond gyrokinetic models: standard gyrokinetic theories ha
 ve their limitations which prevent them to be applied as is to various sit
 uations, in particular in presence of steep gradients as found in the oute
 r plasma region. Advanced kinetic simulations beyond the standard gyrokine
 tic approach used in magnetic fusion will be presented. The relation betwe
 en (fully-)kinetic, gyrokinetic, drift-kinetic and the MHD limit of these 
 will be discussed. In all three sessions, the latest HPC applications in t
 he field will be emphasized.\n\nScientific Machine Learning to Optimize Pl
 asma Turbulence Simulations\n\nControlled fusion offers the promise of sus
 tainable and safe energy production on Earth. In magnetic fusion devices, 
 the power gain increases nonlinearly with the energy confinement time. The
  quality of the plasma energy confinement thus largely determines the size
  and therefore the cost of a fusion...\n\n\nVirginie Grandgirard (CEA); Da
 vid Zarzoso (CNRS); Robin Varennes (National University of Singapore); and
  Feda Almuhisen, Kevin Obrejan, and Julien Bigot (CEA)\n------------------
 ---\nExploration of Quantum Computing for Fusion Energy Science Applicatio
 ns\n\nQuantum computing promises to deliver large gains in computational p
 ower that can potentially benefit a number of Fusion Energy Science (FES) 
 application areas. We will review our recent efforts [1] to develop and ex
 tend quantum algorithms to perform both classical and quantum FES-relevant
  calculati...\n\n\nIlon Joseph (Lawrence Livermore National Laboratory)\n-
 --------------------\nArtificial Intelligence/Machine Learning/HPC Acceler
 ation of Progress in Fusion Energy R&D\n\nThe US goal (March, 2022) to del
 iver a Fusion Pilot Plant [1] has underscored urgency for accelerating the
  fusion energy development timeline.  This will rely heavily on validated 
 scientific and engineering advances driven by HPC together with advanced s
 tatistical methods featuring artificial intell...\n\n\nWilliam Tang (Princ
 eton University, Princeton Plasma Physics Lab)\n---------------------\nTow
 ards Neural Green's Operators for Magnetic Fusion\n\nOperator networks hav
 e emerged as promising machine learning tools for reduced order modeling o
 f a wide range of physical systems described by partial differential equat
 ions (PDEs).  This work describes a new architecture for operator networks
  that approximates the Green's operator to a linear PDE. ...\n\n\nMichael 
 Abdelmalik (Eindhoven University of Technology); Jonathan Citrin (DeepMind
 ); and Josefine Proll, Joost Prins, and Hugo Melchers (Eindhoven Universit
 y of Technology)\n\nDomain: Physics, Computational Methods and Applied Mat
 hematics\n\nSession Chairs: Stephan Brunner (EPFL); Eric Sonnendrücker (Ma
 x Planck Institute for Plasma Physics, Technical University of Munich); an
 d Laurent Villard (EPFL)
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