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DTSTAMP:20181221T160905Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T083000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess322_post146@linklings.com
SUMMARY:DeepSim-HiPAC: Deep Learning High Performance Approximate Calculat
 ion for Interactive Design and Prototyping
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nDeepSim-Hi
 PAC: Deep Learning High Performance Approximate Calculation for Interactiv
 e Design and Prototyping\n\nAl-Jarro, Georgescu, Tomita, Nakashima\n\nWe p
 resent a data-driven technique that can learn from physical-based simulati
 ons for the instant prediction of field distribution for 3D objects. Such 
 techniques are extremely useful when considering, for example, computer ai
 ded engineering (CAE), where computationally expensive simulations are oft
 en required. To accelerate this process, we propose a deep learning framew
 ork that can predict the principal field distribution given a 3D object. T
 his work allows us to learn a system's response using simulation data of a
 rbitrarily shaped objects and an auto-encoder inspired deep neural network
  that maps the input of the 3D object shape to its principal 3D field dist
 ribution. We show that our engine, DeepSim-HiPAC, can estimate field distr
 ibution for two distinctive applications: micro-magnetics design in comput
 ational electromagnetics (CEM) and interactive cooling systems design in c
 omputational fluid dynamics (CFD), several orders of magnitude faster, up 
 to 250000X, than the native calculations and at a cost of low error rate.
URL:https://sc18.supercomputing.org/presentation/?id=post146&sess=sess322
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