no code implementations • 27 Feb 2020 • Christian Himpe
In this work, the empirical-Gramian-based model reduction methods: Empirical poor man's truncated balanced realization, empirical approximate balancing, empirical dominant subspaces, empirical balanced truncation, and empirical balanced gains are compared in a non-parametric and 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, and modified induced dual norms, for variants of the thermal block model reduction benchmark.
Optimization and Control Numerical Analysis Systems and Control Systems and Control Numerical Analysis 93A15, 93B11, 65Y20 G.4
1 code implementation • 21 Sep 2018 • Peter Benner, Christian Himpe
A standard approach for model reduction of linear input-output systems is balanced truncation, which is based on the controllability and observability properties of the underlying system.
Optimization and Control Systems and Control Numerical Analysis 93A15, 93B11, 93B20
1 code implementation • 18 Jul 2016 • Christian Himpe, Tobias Leibner, Stephan Rave
Proper Orthogonal Decomposition (POD) is a widely used technique for the construction of low-dimensional approximation spaces from high-dimensional input data.
Numerical Analysis
no code implementations • 29 Jan 2013 • Christian Himpe, Mario Ohlberger
A common approach in model reduction is balanced truncation, which is based on gramian matrices classifiying certain attributes of states or parameters of a given dynamic system.
Optimization and Control Mathematical Software Systems and Control Dynamical Systems 93c99 G.1.3
