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SUMMARY:Active Subspace Techniques to Construct Surrogate Models for Compl
 ex Physical and Biological Models - Ralph  Smith (North Carolina State Uni
 versity)
DTSTART:20180205T143000Z
DTEND:20180205T153000Z
UID:TALK99868@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:For many complex physical and biological models\, the computat
 ional cost of high-fidelity simulation codes precludes their direct use fo
 r Bayesian model calibration and uncertainty propagation.  For example\, t
 he considered neutronics and nuclear thermal hydraulics codes can take hou
 rs to days for a single run.  Furthermore\, the models often have tens to 
 thousands of inputs--comprised of parameters\, initial conditions\, or bou
 ndary conditions--many of which are unidentifiable in the sense that they 
 cannot be uniquely determined using measured responses. In this presentati
 on\, we will discuss techniques to isolate influential inputs for subseque
 nt surrogate model construction for Bayesian inference and uncertainty pro
 pagation.  For input selection\, we will discuss advantages and shortcomin
 gs of global sensitivity analysis to isolate influential inputs and the us
 e of active subspace construction to determine low-dimensional input manif
 olds.  We will also discuss the manner in which Bayesian calibration on ac
 tive subspaces can be used to quantify uncertainties in physical parameter
 s.  These techniques will be illustrated for models arising in nuclear pow
 er plant design\, quantum-informed material characterization\, and HIV mod
 eling and treatment.
LOCATION:Seminar Room 1\, Newton Institute
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