Date: Tuesday 15 November 2022, 15:00 (Europe/London)
Speaker: Dr Małgorzata J. Zimoń , Research Staff Member at IBM Research UK
Abstract
Recent advances in simulations and big data processing bring the promise of digital twins, composable virtual representations of physical systems, and their impact on society closer to reality. One important enabling technology is surrogate modelling. The basic idea in the surrogate model approach is to invest resources in developing fast mathematical approximations to the long running computer codes or expensive physical experiments. Given these approximations, many questions can be posed and answered, many trade-offs explored, and insights gained. In this presentation, I will discuss different types of surrogate models and how they can be utilised advantageously in digital twin framework. I will focus on the physics-informed data-driven models and their application to uncertainty quantification and system prediction for industrial applications.
Biography
Małgorzata holds a research position at IBM Research UK. She completed her PhD study in the Department of Mechanical & Aerospace Engineering at the University of Strathclyde in Scotland jointly with STFC Daresbury Laboratory in England. Soon after obtaining her degree, she received the Doctoral Research Award allowing her to continue the research at the School of Mechanical, Aerospace and Civil Engineering in Manchester. After a year of working at the University of Manchester, she joined the newly established IBM Research group at Daresbury Laboratory. Her interests involve investigating filtering algorithms and performing uncertainty quantification in the framework of computational multi-scale/multi-physics modelling.
You can now watch the seminar on YouTube: https://youtu.be/FRgTha6l7c0
