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SUMMARY:Population-Based Inference in Mechanics - Arnaud Vadeboncoeur\, Un
 iversity of Cambridge
DTSTART:20250207T150000Z
DTEND:20250207T160000Z
UID:TALK228076@talks.cam.ac.uk
CONTACT:Shehara Perera
DESCRIPTION:Inferring model parameters from observational data of a physic
 al system is the setup for many inverse problems.\nSolving these kinds of 
 problems can give key insight into the state of a system for quantities th
 at are not directly observable\, such as material properties. In this talk
 \, we discuss a population-based perspective on\nsolving inverse problems 
 where the data available comes from a collection of physical systems and w
 e are interested in characterising the (indirectly observable)\nproperties
  of these systems at a distributional level. We call this: calibrating pri
 ors from indirect data. Furthermore\, we show how this can be accomplished
  while\nconcurrently learning ML-based surrogates which capture the behavi
 our of the physical systems of interest.
LOCATION:CivEng Seminar Room (1-33) (Civil Engineering Building)
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