Comparing (Empirical-Gramian-Based) Model Order Reduction Algorithms

In this work, the empirical-Gramian-based model reduction methods: Empirical poor man’s truncated balanced realization, empirical approximate balancing, dominant subspaces, truncation, and gains are compared in a non-parametric two parametric variants, via ten error measures: Approximate Lebesgue \(L_0\), \(L_1\), \(L_2\), \(L_\infty \), Hardy \(H_2\), \(H_\infty Hankel, Hilbert-Schmidt-Hankel, modified induced primal, dual norms, for variants of thermal block benchmark. This comparison is conducted new meta-measure reducibility called MORscore.