Date: 28 February 2024
Time: 12.45 Hours
Room: RA1501 & online
Speaker: dr. P (Patrick) Buchfink (DAMUT-MAST)
Title: “Structure-preserving MOR on submanifolds and subspaces"
Abstract:
Model order reduction (MOR) is a method to reduce the computational cost of high-dimensional models by approximating it with a low-dimensional surrogate model. Such techniques are required to evaluate high-dimensional models (a) many times (e.g. for parameter studies or optimization), (b) in real-time (e.g. for model-based control), or (c) on devices with low computational power (e.g. for embedded devices). Recently, machine learning methods have been investigated to enable MOR on submanifolds, which can improve the approximation quality and broaden the applicability of MOR to problems that cannot be treated efficiently with the (classical) MOR on subspaces.
In this talk we present a differential geometric framework for MOR on submanifolds and use it to formulate structure-preserving MOR techniques for Hamiltonian systems. Moreover, we give an overview of existing approaches for MOR on submanifolds and how to classify these approaches within our framework.