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UID:20261009T104837EDT-7864edeRBO@132.216.98.100
DTSTAMP:20261009T144837Z
DESCRIPTION:Title: Formulation and solution of stochastic inverse problems 
 for science and engineering models\n\nAbstract: The stochastic inverse pro
 blem of determining probability structures on input parameters for a physi
 cs model corresponding to a given probability structure on the output of t
 he model forms the core of scientific inference and engineering design. We
  describe a formulation and solution method for stochastic inverse problem
 s that is based on functional analysis\, differential geometry\, and proba
 bility/measure theory. This approach yields a computationally tractable pr
 oblem while avoiding alterations of the model like regularization and ad h
 oc assumptions about the probability structures. We present several exampl
 es\, including a high-dimensional application to determination of paramete
 r fields in storm surge models. We also describe work aimed at defining a 
 notion of condition for stochastic inverse problems and tackling the relat
 ed problem of designing sets of optimal observable quantities.\n
DTSTART:20191122T210000Z
DTEND:20191122T220000Z
LOCATION:Room PK-5115 \, CA\, Pavillon President-Kennedy\, 201 Ave. Preside
 nt-Kennedy
SUMMARY:Donald Estep (Simon Fraser University)
URL:https://www.mcgill.ca/mathstat/channels/event/donald-estep-simon-fraser
 -university-302634
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