Model-Order Reduction of Magnetoquasi-Static Problems Based on POD and Arnoldi-Based Krylov Methods

Abstract

The proper orthogonal decomposition method and Arnoldi-based Krylov projection method are investigated in order to reduce a finite-element model of a quasi-static problem. Both methods are compared on an academic example in terms of computation time and precision.Index Terms-Krylov subspace, model-order reduction, proper orthogonal decomposition, quasi-static problem.

Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

“…Recently, the application of projection-based surrogate methods in electromagnetic devices has been of interest to a large number of researchers in the field. For example, POD and Arnoldi-based Krylov methods have been used to efficiently reduce the order of linear electromagnetic problems [19]- [21].…”

Multi-Port Model Order Reduction Using a Matrix Cauer Ladder Network

“…For an efficient representation of the EC field, several model order reduction (MOR) methods have been developed, such as the PVL method [1]- [4] and POD method [5], [6]. The CLN method [7], [8] is an energy-based MOR method that provides a clear physical interpretation of the network elements.…”

POD-based model order reduction with an adaptive snapshot selection for a discontinuous Galerkin approximation of the time-domain Maxwell's equations

“…The alternative approach that we consider in this work is model order reduction (MOR) [2,13,14,15]. MOR method is useful for accelerating simulations in many fields of science and engineering [16,17,18,19,20,21,22]. In particular, MOR method is also widely used in the context of electromagnetics [19,23,24,25,26,27,28,29,30,31,32].…”