Apr 2022
Abstract:
One approach to build projected parametric reduced order models is via interpolation in manifolds. The sampled inputs stem from the set of fixed rank POD matrices that form the so-called matrix manifold. Interpolation of such points allows to account for the geometrical structure of the POD data and thus to produce an accurate low order approximation of the underlying problem at new untrained parameter points. The aim of this talk is to give an overview of these adaptation techniques and highlight their application in parametric physical problems.
Bio:
Mourad Oulghelou is currently an assistant professor in Fluid Mechanics at Arts et Métiers ParisTech, obtained a Phd in Fluid Mechanics (2018 Development of Adaptive Model Order Reduction techniques for Optimal Flow Control) and a Master's degree in Applied Mathematics (2014). His research is focused on adaptation methods for reduced order parametric problems, interpolation of reduced order bases, Data-Driven approaches for flow prediction and real-time optimal control of flow problems.