Abstract: Along the ideas of Curtain and Glover (in: Bart, Gohberg, Kaashoek (eds) Operator theory and systems, Birkhäuser, Boston, 1986), we extend the balanced truncation method for (infinite-dimensional) linear systems to arbitrary-dimensional bilinear and stochastic systems. In particular, we apply Hilbert space techniques used in many-body quantum mechanics to establish new fully explicit error bounds for the truncated system and prove convergence results. The functional analytic setting allows us to obtain mixed Hardy space error bounds for both finite-and infinite-dimensional systems, and it is then applied to the model reduction of stochastic evolution equations driven by Wiener noise
When solving partial differential equations numerically, usually a high order spatial discretization...
The aim of this book is to give a systematic and self-contained presentation of the basic results on...
Balanced truncation is one of the most common model reduction schemes. In this note, we present a su...
Along the ideas of Curtain and Glover, we extend the balanced truncation method for infinite-dimensi...
In this paper, we investigate a large-scale stochastic system with bilinear drift and linear diffusi...
We analyze structure-preserving model order reduction methods for Ornstein-Uhlenbeck processes and l...
The rate of H-convergence of truncations of stochastic infinite-dimensional systems du = [Au+B(u)]dt...
We study a class of balanced truncation algorithms applicable to relative/multiplicative model reduc...
When solving linear stochastic differential equations numerically, usually a high order spatial disc...
Bounded real balanced truncation for infinite-dimensional systems is considered. This provides reduc...
Recently, an adaptation of the balanced truncation method for model reduction of classical (non-quan...
In this paper, we investigate a large-scale stochastic system with bilinear drift and linear diffusi...
Balanced truncation of discrete linear time-invariant systems is an automatic method once an error t...
AbstractWe study linear stochastic evolution partial differential equations driven by additive noise...
The purpose of this paper is to develop a model reduction theory for linear quantum stochastic syste...
When solving partial differential equations numerically, usually a high order spatial discretization...
The aim of this book is to give a systematic and self-contained presentation of the basic results on...
Balanced truncation is one of the most common model reduction schemes. In this note, we present a su...
Along the ideas of Curtain and Glover, we extend the balanced truncation method for infinite-dimensi...
In this paper, we investigate a large-scale stochastic system with bilinear drift and linear diffusi...
We analyze structure-preserving model order reduction methods for Ornstein-Uhlenbeck processes and l...
The rate of H-convergence of truncations of stochastic infinite-dimensional systems du = [Au+B(u)]dt...
We study a class of balanced truncation algorithms applicable to relative/multiplicative model reduc...
When solving linear stochastic differential equations numerically, usually a high order spatial disc...
Bounded real balanced truncation for infinite-dimensional systems is considered. This provides reduc...
Recently, an adaptation of the balanced truncation method for model reduction of classical (non-quan...
In this paper, we investigate a large-scale stochastic system with bilinear drift and linear diffusi...
Balanced truncation of discrete linear time-invariant systems is an automatic method once an error t...
AbstractWe study linear stochastic evolution partial differential equations driven by additive noise...
The purpose of this paper is to develop a model reduction theory for linear quantum stochastic syste...
When solving partial differential equations numerically, usually a high order spatial discretization...
The aim of this book is to give a systematic and self-contained presentation of the basic results on...
Balanced truncation is one of the most common model reduction schemes. In this note, we present a su...