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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20200129T163603Z
LOCATION:502-503-504
DTSTART;TZID=America/Denver:20191117T143000
DTEND;TZID=America/Denver:20191117T150000
UID:submissions.supercomputing.org_SC19_sess101_ws_dls119@linklings.com
SUMMARY:Deep Learning for Gap Crossing Ability of Ground Vehicles
DESCRIPTION:Workshop\n\nDeep Learning for Gap Crossing Ability of Ground V
 ehicles\n\nParsons, Cheng\n\nIn this work we present our results designing
  a deep neural network (DNN) to act as a surrogate model for costly HPC si
 mulations. In order to determine a ground vehicle's gap crossing ability i
 n extreme weather scenarios, several HPC simulations are currently used. H
 ydrologic models are first run to determine the environmental conditions o
 ver an area of interest.  Once these conditions are known they are given, 
 along with the terrain data, to a vehicle simulation which determines if a
  particular vehicle can cross a stream at a given point. Every point of in
 terest must be evaluated independently, which quickly becomes infeasible f
 or a large numbers of crossing points. In order to accelerate this phase o
 f the process, we have created a DNN that acts as a surrogate model for th
 e vehicle simulator. Despite several challenges converting irregular data 
 into a form that can be used with a DNN, and incorporating scalars into th
 e models, we were able to produce DNN models that predicted the gap crossi
 ng ability of all vehicle types with over 95% accuracy.\n\nTag: Workshop R
 eg Pass, Deep Learning, Scientific Computing\n\nRegistration Category: Wor
 kshop Reg Pass, Deep Learning, Scientific Computing
URL:https://sc19.supercomputing.org/presentation/?id=ws_dls119&sess=sess10
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