149 citations · 609 across the 25 of their papers we have counts for
30 papers
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…
Port-Hamiltonian Realizations of Nonminimal Linear Time Invariant Systems
Christopher Beattie, Volker Mehrmann, Hongguo Xu
Numerical methods for developing port-Hamiltonian representations of general linear time-invariant systems are studied. The approach extends previous port-Hamiltonian characterizat…
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference
Elizabeth Qian, Jemima M. Tabeart, Christopher Beattie +4
We consider the Bayesian approach to the linear Gaussian inference problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken afte…
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…