CHIMERA seminar with Dirk Husmeier

Abstract

I will be giving an overview of some of the work we're doing at SofTMech-Set, with a focus on inference in complex cardio-physiological systems. This is motivated by work on fluid dynamics, with an application to modelling pulmonary hypertension (high blood pressure in the lungs), and cardiac mechanics, with an application to modelling myocardial infarction (heart attack). After a very brief introduction to the mathematical models, I will first discuss the standard statistical approach to parameter estimation, and explain why this is computationally challenging or even intractable for biophysical systems of the given complexity. I will then discuss an alternative method from machine learning, based on surrogate modelling and statistical emulation. I will apply this framework to model calibration and parameter estimation, and will discuss forward versus inverse uncertainty quantification. If time permits, I will conclude with an outlook on recent work on message passing and graph neural networks.