149 citations · 609 across the 25 of their papers we have counts for
20 papers · 1 filter
A non-intrusive data-based reformulation of a hybrid projection-based model reduction method
Ion Victor Gosea, Serkan Gugercin, Christopher Beattie
We present a novel data-driven reformulation of the iterative SVD-rational Krylov algorithm (ISRK), in its original formulation a Petrov-Galerkin (two-sided) projection-based itera…
IRKA is a Riemannian Gradient Descent Method
Petar Mlinarić, Christopher A. Beattie, Zlatko Drmač +1
The iterative rational Krylov algorithm (IRKA) is a commonly used fixed-point iteration developed to minimize the model order reduction error. In this work, IRKA is…
Data-driven balancing of linear dynamical systems
Ion Victor Gosea, Serkan Gugercin, Christopher Beattie
We present a novel reformulation of balanced truncation, a classical model reduction method. The principal innovation that we introduce comes through the use of system response dat…
Sampling-free parametric model reduction for structured systems
Christopher Beattie, Serkan Gugercin, Zoran Tomljanovic
We consider the reduction of parametric families of linear dynamical systems having an affine parameter dependence that differ from one another by a low-rank variation in the state…
Revisiting IRKA: Connections with pole placement and backward stability
C. Beattie, Z. Drmac, S. Gugercin
The iterative rational Krylov algorithm (\textsf{IRKA}) is a popular approach for producing locally optimal reduced-order -approximations to linear time-invariant (L…
Structure-preserving Interpolatory Model Reduction for Port-Hamiltonian Differential-Algebraic Systems
Chris A. Beattie, Serkan Gugercin, Volker Mehrmann
We examine interpolatory model reduction methods that are well-suited for treating large scale port-Hamiltonian differential-algebraic systems in a way that is able to preserve and…