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SUMMARY:Experimental Design for Prediction of Physical System Means Using 
 Calibrated Computer Simulators - Angela Dean (Ohio State University)
DTSTART:20180411T080000Z
DTEND:20180411T090000Z
UID:TALK103633@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Computer experiments using deterministic simulators are often 
 used to supplement physical system experiments.  A common problem is that 
 a computer simulator may provide biased output for the physical process du
 e to the simplified physics or biology used in the mathematical model.  Ho
 wever\, when physical observations are available\, it may be possible to u
 se these data to align the simulator output to be close to the true mean r
 esponse by constructing a bias-corrected predictor (a process called calib
 ration).  This talk looks at two aspects of experimental design for predic
 tion of physical system means using a Bayesian calibrated predictor.  Firs
 t\, the empirical prediction accuracy over the output space of several dif
 ferent types of combined physical and simulator designs is discussed.  In 
 particular\, designs constructed using the integrated mean squared predict
 ion error seem to perform well.  Secondly\, a sequential design methodolog
 y for optimizing a physical manufacturing process when there are multiple\
 , competing product objectives is described.   The goal is to identify a s
 et of manufacturing conditions each of which leads to outputs on the Paret
 o Front of the product objectives\, i.e. identify manufacturing conditions
  which cannot be modified to improve all the product objectives simultaneo
 usly.  A sequential design methodology which maximizes the posterior expec
 ted minimax fitness function is used to add data from either the simulator
  or the manufacturing process.  The method is illustrated with an example 
 from an injection molding study.  The presentation is based on joint work 
 with Thomas Santner\, Erin Leatherman\, and Po-Hsu Allen Chen.
LOCATION:Seminar Room 1\, Newton Institute
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