Serkan Gugercin

Professor

Virginia Tech

United States

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About

Serkan Gugercin is a Professor at Virginia Tech in the United States. His research encompasses various areas, including time-domain iterative methods, structured interpolation, and reduced-order modeling of linear and nonlinear systems. Recent contributions include articles on topics such as Riemannian gradient descent methods, energy functions for nonlinear balanced truncation, and data-driven reduced modeling.

Articles (21)

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Collaborators (7)

Peter Benner

Adjunct Professor

Otto-von-Guericke Universität Magdeburg

GERMANY

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Vijaya Venkata Narasimha Sriram Malladi

Assistant professor

Michigan Technological University

UNITED STATES

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Steffen W. R. Werner

Assistant Professor

Virginia Tech

UNITED STATES

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Jemima Tabeart

Eindhoven University of Technology

NETHERLANDS

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Christopher Beattie

Professor

Virginia Tech

UNITED STATES

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Peter Kramer

Professor

Rensselaer Polytechnic Institute

UNITED STATES

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Boris Kramer

Assistant Professor

University of California, San Diego

UNITED STATES

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Serkan Gugercin

Research Interests

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About

Articles (21)

Interpolatory \(\boldsymbol {{\mathcal {H}}_{2}}\)-optimality Conditions for Structured Linear Time-invariant Systems

Time-Domain Iterative Rational Krylov Method

IRKA Is a Riemannian Gradient Descent Method

Frequency-Based Reduced Models from Purely Time-Domain Data via Data Informativity

Balanced truncation with conformal maps

Structured interpolation for multivariate transfer functions of quadratic-bilinear systems

Scalable computation of energy functions for nonlinear balanced truncation

Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems

Structure‐preserving interpolation of quadratic‐bilinear systems via regular multivariate transfer functions

Frequency-Based Reduced Models from Purely Time-Domain Data via Data Informativity

A Unifying Framework for Interpolatory \({\boldsymbol{\mathcal{L}_2}}\)-Optimal Reduced-Order Modeling

The p-AAA Algorithm for Data-Driven Modeling of Parametric Dynamical Systems

Contour Integral Methods for Nonlinear Eigenvalue Problems: A Systems Theoretic Approach

\(\boldsymbol{\mathcal{L}_2}\)-Optimal Reduced-Order Modeling Using Parameter-Separable Forms

A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations

A unifying framework for tangential interpolation of structured bilinear control systems

Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference

Data-Driven Modeling of Linear Dynamical Systems with Quadratic Output in the AAA Framework

Data-Driven Balancing of Linear Dynamical Systems

Estimating experimental dispersion curves from steady-state frequency response measurements

Structure-preserving interpolation of bilinear control systems

Collaborators (7)

Peter Benner

Vijaya Venkata Narasimha Sriram Malladi

Steffen W. R. Werner

Jemima Tabeart

Christopher Beattie

Peter Kramer

Boris Kramer

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