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DTSTAMP:20260522T150111Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post146@linklings.com
SUMMARY:DeepSim-HiPAC: Deep Learning High Performance Approximate Calculat
 ion for Interactive Design and Prototyping
DESCRIPTION:Ahmed Al-Jarro and Serban Georgescu (Fujitsu Laboratories of E
 urope Ltd.) and Yasumoto Tomita and Kouta Nakashima (Fujitsu Laboratories 
 Ltd)\n\nWe present a data-driven technique that can learn from physical-ba
 sed simulations for the instant prediction of field distribution for 3D ob
 jects. Such techniques are extremely useful when considering, for example,
  computer aided engineering (CAE), where computationally expensive simulat
 ions are often required. To accelerate this process, we propose a deep lea
 rning framework that can predict the principal field distribution given a 
 3D object. This work allows us to learn a system's response using simulati
 on data of arbitrarily shaped objects and an auto-encoder inspired deep ne
 ural network that maps the input of the 3D object shape to its principal 3
 D field distribution. We show that our engine, DeepSim-HiPAC, can estimate
  field distribution for two distinctive applications: micro-magnetics desi
 gn in computational electromagnetics (CEM) and interactive cooling systems
  design in computational fluid dynamics (CFD), several orders of magnitude
  faster, up to 250000X, than the native calculations and at a cost of low 
 error rate.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg 
 Pass\n\n
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