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Population-Based Inference in Mechanics

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If you have a question about this talk, please contact Shehara Perera .

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

This talk is part of the Engineering Department Structures Research Seminars series.

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LAB 216 (Anglia Ruskin University, Cambridge Campus) Cambridge Institute of Public Health Bateman Auditorium, Gonville & Caius College

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