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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055410Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211114T140000
DTEND;TZID=America/Chicago:20211114T143000
UID:submissions.supercomputing.org_SC21_sess424_misc369@linklings.com
SUMMARY:Lightning Talk: In Situ Anomaly Detection and Reduced Order Surrog
 ate Models for DNS of Turbulent  Combustion
DESCRIPTION:Workshop\n\nLightning Talk: In Situ Anomaly Detection and Redu
 ced Order Surrogate Models for DNS of Turbulent  Combustion\n\nChen\n\nExa
 scale computing will provide a unique opportunity to approach device-scale
  first principles direct numerical simulation (DNS) and enable access to p
 hysics regimes previously unattainable. With the advantages of access to “
 bigger, more complex” problems come challenges of data management and requ
 irements for new tools for data discovery. There is an inherent need to ca
 rry out data discovery and efficiently manage computational requirements i
 n situ while simulations are performed on DOE leadership class machines. M
 oreover, with increasing computational resources, scientists will want to 
 increase the scale and complexity of problems they are interested to inves
 tigate.  Machine learning (ML) has emerged as an integral part of advancin
 g state-of-the-art DNS and extending this valuable simulation approach to 
 the exascale and beyond. On the one hand, strategies are being designed to
  enable the implementation of predictive in situ reduced order models (ROM
 s) that are computationally more efficient than conventional DNS through i
 n situ model reduction and data compression. On the other hand, strategies
  are designed to detect anomalous physics behavior used to computationally
  steer downstream analysis. These strategies will be described in the cont
 ext of combustion simulations at extreme scale.\n\nTag: Online Only, Appli
 cations, Big Data, Data Analytics, Data Management\n\nRegistration Categor
 y: Workshop Reg Pass
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