9 citations · 47 across the 37 of their papers we have counts for
42 papers
Rational approximation for Zolotarev sign and ratio problems
Charles Poussot-Vassal, Ion Victor Gosea, Athanasios C. Antoulas
We study algorithms for Zolotarev (3rd and 4th) problems rational approximation. First, we show that the Loewner framework (LF) is appropriate to rapidly and with no iterations, ap…
Data-driven balanced truncation for linear systems with quadratic outputs
Reetish Padhi, Ion Victor Gosea, Igor Pontes Duff +1
We develop the framework for a non-intrusive, quadrature-based method for approximate balanced truncation (QuadBT) of linear systems with quadratic outputs, thus extending the appl…
Tensor-based multivariate function approximation: methods benchmarking and comparison
Charles Poussot-Vassal, Ion Victor Gosea, Pierre Vuillemin +1
We evaluate some methods designed for tensor- (or data-) based multivariate model construction (approximation and compression). To this aim, a collection of multivariate functions…
-optimal model reduction of linear quadratic-output systems by multivariate rational interpolation
Sean Reiter, Ion Victor Gosea, Igor Pontes Duff +1
This paper addresses the -optimal approximation of linear dynamical systems with quadratic-output functions, also known as linear quadratic-output systems. Our major…
Barycentric rational approximation for learning the index of a dynamical system from limited data
Davide Pradovera, Ion Victor Gosea, Jan Heiland
We consider the task of data-driven identification of dynamical systems, specifically for systems whose behavior at large frequencies is non-standard, as encoded by a non-trivial r…
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…
Snapshot-driven Rational Interpolation of Parametric Systems
Art J. R. Pelling, Karim Cherifi, Ion Victor Gosea +1
Parametric data-driven modeling is relevant for many applications in which the model depends on parameters that can potentially vary in both space and time. In this paper, we prese…
optimal model reduction of linear systems with multiple quadratic outputs
Sean Reiter, Igor Pontes Duff, Ion Victor Gosea +1
In this work, we consider the optimal model reduction of dynamical systems that are linear in the state equation and up to quadratic nonlinearity in the output equation. As o…
On the Loewner framework, the Kolmogorov superposition theorem, and the curse of dimensionality
Athanasios C. Antoulas, Ion Victor Gosea, Charles Poussot-Vassal
The Loewner framework is an interpolatory approach for the approximation of linear and nonlinear systems. The purpose here is to extend this framework to linear parametric systems…
Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
Ion Victor Gosea, Luisa Peterson, Pawan Goyal +3
In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This…
A modified AAA algorithm for learning stable reduced-order models from data
Tommaso Bradde, Stefano Grivet-Talocia, Quirin Aumann +1
In recent years, the Adaptive Antoulas-Anderson AAA algorithm has established itself as the method of choice for solving rational approximation problems. Data-driven Model Order Re…
Generalizations of data-driven balancing: What to sample for different balancing-based reduced models
Sean Reiter, Ion Victor Gosea, Serkan Gugercin
The quadrature-based balanced truncation (QuadBT) framework of arXiv:2104.01006 is a non-intrusive reformulation of balanced truncation (BT), a classical projection-based model-ord…
Implicit and explicit matching of non-proper transfer functions in the Loewner framework
Ion Victor Gosea, Jan Heiland
The reduced-order modeling of a system from data (also known as system identification) is a classical task in system and control theory and well understood for standard linear syst…
Loewner functions for bilinear systems
Pauline Kergus, Ion Victor Gosea, Mihaly Petreczky
This work brings together the moment matching approach based on Loewner functions and the classical Loewner framework based on the Loewner pencil in the case of bilinear systems. N…
Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems
Ion Victor Gosea, Serkan Gugercin, Steffen W. R. Werner
An essential tool in data-driven modeling of dynamical systems from frequency response measurements is the barycentric form of the underlying rational transfer function. In this wo…
Data-driven and low-rank implementations of Balanced Singular Perturbation Approximation
Björn Liljegren-Sailer, Ion Victor Gosea
Balanced Singular Perturbation Approximation (SPA) is a model order reduction method for linear time-invariant systems that guarantees asymptotic stability and for which there exis…
Practical challenges in data-driven interpolation: dealing with noise, enforcing stability, and computing realizations
Quirin Aumann, Ion Victor Gosea
In this contribution, we propose a detailed study of interpolation-based data-driven methods that are of relevance in the model reduction and also in the systems and control commun…
Approximating a flexible beam model in the Loewner framework
A. Zuyev, I. V. Gosea
The paper develops the Loewner approach for data-based modeling of a linear distributed-parameter system. This approach is applied to a controlled flexible beam model coupled with…
Data-driven quadratic modeling in the Loewner framework from input-output time-domain measurements
D. S. Karachalios, I. V. Gosea, L. Gkimisis +1
In this study, we present a purely data-driven method that uses the Loewner framework (LF) along with nonlinear optimization techniques to infer quadratic with affine control dynam…
Bilinear realization from input-output data with neural networks
Dimitrios S. Karachalios, Ion Victor Gosea, Kirandeep Kour +1
We present a method that connects a well-established nonlinear (bilinear) identification method from time-domain data with neural network (NNs) advantages. The main challenge for f…
Data-driven approximation and reduction from noisy data in matrix pencil frameworks
Pauline Kergus, Ion Victor Gosea
This work aims at tackling the problem of learning surrogate models from noisy time-domain data by means of matrix pencil-based techniques, namely the Hankel and Loewner frameworks…
A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits
Dimitrios S. Karachalios, Ion Victor Gosea, Athanasios C. Antoulas
In this contribution, we propose a data-driven procedure to fit quadratic-bilinear surrogate models from data. Although the dynamics characterizing the original model are strongly…
Structured vector fitting framework for mechanical systems
Steffen W. R. Werner, Ion Victor Gosea, Serkan Gugercin
In this paper, we develop a structure-preserving formulation of the data-driven vector fitting algorithm for the case of modally damped mechanical systems. Using the structured pol…
Data-driven modeling and control of large-scale dynamical systems in the Loewner framework
Ion Victor Gosea, Charles Poussot-Vassal, Athanasios C. Antoulas
In this contribution, we discuss the modeling and model reduction framework known as the Loewner framework. This is a data-driven approach, applicable to large-scale systems, which…