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DTSTAMP:20211207T055401Z
LOCATION:225
DTSTART;TZID=America/Chicago:20211115T161000
DTEND;TZID=America/Chicago:20211115T165000
UID:submissions.supercomputing.org_SC21_sess349_ws_isav104@linklings.com
SUMMARY:In-Situ Spatial Inference on Climate Simulations with Sparse Gauss
 ian Processes
DESCRIPTION:Workshop\n\nIn-Situ Spatial Inference on Climate Simulations w
 ith Sparse Gaussian Processes\n\nGrosskopf, Lawrence, Biswas, Tang, Rumsey
 ...\n\nAs extreme-scale physics simulation becomes increasingly memory and
  storage expensive, the ability to access full simulation data for statist
 ical analysis is becoming increasingly limited. The capability to perform 
 in-situ statistical inference of state variables is becoming increasingly 
 important for the comprehensive utilization of the huge amounts of informa
 tion generated by these simulations. In this work, we report the first res
 ults fitting scalable Gaussian process regression to the state information
  of an expensive simulation in-situ. For this, spatial regression of near 
 surface temperature data was performed using Julia coupled to the E3SM cli
 mate model. The resulting sparse Gaussian process model shows strong predi
 ctive performance using a small number of representative observations. The
 se results provide the backbone for more general in-situ spatial inference
  with Gaussian process models in complex physics simulations.\n\nTag: Appl
 ications, Big Data, Visualization\n\nRegistration Category: Workshop Reg P
 ass
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