9 citations · 47 across the 37 of their papers we have counts for
8 papers · 1 filter
Learning reduced-order models of quadratic control systems from input-output data
Ion Victor Gosea, Dimitrios S. Karachalios, Athanasios C. Antoulas
In this paper, we address an extension of the Loewner framework for learning quadratic control systems from input-output data. The proposed method first constructs a reduced-order…
On Loewner data-driven control for infinite-dimensional systems
Ion Victor Gosea, Charles Poussot-Vassal, Athanasios C. Antoulas
In this paper, we address extensions of the Loewner Data-Driven Control (L-DDC) methodology. First, this approach is extended by incorporating two alternative approximation methods…
The AAA framework for modeling linear dynamical systems with quadratic output
Ion Victor Gosea, Serkan Gugercin
We consider linear dynamical systems with quadratic output. We first define the two transfer functions, a single-variable and a multivariate one, that fully describe the dynamics o…
Rational approximation of the absolute value function from measurements: a numerical study of recent methods
Ion Victor Gosea, Athanasios C. Antoulas
In this work, we propose an extensive numerical study on approximating the absolute value function. The methods presented in this paper compute approximants in the form of rational…
Model reduction of linear hybrid systems
Ion Victor Gosea, Mihaly Petreczky, John Leth +2
The paper proposes a model reduction algorithm for linear hybrid systems, i.e., hybrid systems with externally induced discrete events, with linear continuous subsystems, and linea…
On Bilinear Time Domain Identification and Reduction in the Loewner Framework
D. S. Karachalios, I. V. Gosea, A. C. Antoulas
The Loewner framework-(LF) in combination with Volterra series-(VS) offers a non-intrusive approximation method that is capable of identifying bilinear models from time-domain meas…
Toward fitting structured nonlinear systems by means of dynamic mode decomposition
Ion Victor Gosea, Igor Pontes Duff
The dynamic mode decomposition (DMD) is a data-driven method used for identifying the dynamics of complex nonlinear systems. It extracts important characteristics of the underlying…
Algorithms for the rational approximation of matrix-valued functions
Ion Victor Gosea, Stefan Güttel
A selection of algorithms for the rational approximation of matrix-valued functions are discussed, including variants of the interpolatory AAA method, the RKFIT method based on app…