Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem

Abstract

Uncertainty Quantification (UQ) is an important field to quantify the propagation of uncertainties, analyze sensitivities or realize statistical inversion of a mathematical model. Sampling-based estimation techniques evaluate the model for many different parameter samples. For computationally intensive models, this might require long runtimes or even be infeasible. This so-called multi-query problem can be speeded up or even be enabled with surrogate models from model order reduction (MOR) techniques. For accurate and physically consistent MOR, structure-preserving reduction is essential.