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DTSTAMP:20260522T150128Z
LOCATION:D171
DTSTART;TZID=America/Chicago:20181114T133000
DTEND;TZID=America/Chicago:20181114T141500
UID:submissions.supercomputing.org_SC18_sess481_pec263@linklings.com
SUMMARY:Data Fusion for Nuclear Fusion – Using HPC To Put a Star in a Bott
 le
DESCRIPTION:Sean Dettrick (TAE Technologies)\n\nFusion energy offers the p
 rospect of a carbon-neutral, environmentally responsible, and inexhaustibl
 e energy source.  TAE Technologies Inc. is trying to greatly accelerate fu
 sion research to develop the world’s first commercially viable fusion-powe
 red generator for electricity production. \n\nTo this end, TAE has investe
 d $100 million of capital expenditure in the construction of its latest ma
 gnetically confined fusion experiment, “Norman,” an advanced beam-driven f
 ield-reversed configuration (FRC) plasma device.  A central challenge of u
 nderstanding the physics in a fusion plasma experiment is that most of the
  experimental diagnostics are indirect in nature and require inverse probl
 ems, such as tomographic inversion, and that there are many interacting de
 grees of freedom each of which requires its own diagnostic and inversion p
 rocess.  To understand the “what” of the complete plasma state measured in
  this way requires data science, in particular, sensor fusion and Bayesian
  Inference.  To understand the “why” of the plasma state requires theory a
 nd advanced computation, which is made challenging by multiple time and sp
 ace scales and multi-physics interactions.  The central problem of keeping
  the plasma hot enough for long enough to achieve fusion cannot be address
 ed without using HPC to understand non-linear wave-particle interactions, 
 which can bring both great benefit in terms of kinetic stabilization of mo
 des at the macroscale, and detriments in the form of heat losses due to ki
 netic microturbulence. \n\nAnalysis of existing experiments, and predictio
 n of performance in new parameter regimes, requires a fusion of the “what”
  and the “why” with a combination of data science and HPC modeling.  TAE i
 s partnering with data science heavyweights – Google and others – and big 
 iron heavyweights such as the Department of Energy Leadership Computing Fa
 cilities – to bring the most advanced data science algorithms and the fast
 est computers to bear on these problems.\n\nTag: Industry\n\nRegistration 
 Category: Workshop Reg Pass, Tutorial Reg Pass, Tech Program Reg Pass, Exh
 ibits Reg Pass, Exhibits - Exhibit Hall Only Reg Pass\n\nSession Chairs: L
 yle Long (Pennsylvania State University) and David Martin (Lawrence Berkel
 ey National Laboratory (LBNL), Energy Sciences Network (ESnet))\n\n
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