World's Best Scientists 2026 revealed!
Serkan Gugercin

Serkan Gugercin

D-Index & Metrics

Mathematics

D-Index
40
Citations
9953
World Ranking
1999
National Ranking
843

Engineering and Technology

D-Index
40
Citations
9965
World Ranking
7170
National Ranking
1952

Overview

Serkan Gugercin is affiliated with Virginia Tech in the United States. Their research primarily focuses on engineering and intersects extensively with physics and astronomy. Within these broad disciplines, Gugercin's work spans several subfields, including statistical and nonlinear physics, control and systems engineering, statistics, probability and uncertainty, numerical analysis, and computational mechanics.

The scientist's main research topics cover a range of areas relating to dynamic systems and computational methods. Key topics include model reduction and neural networks, control systems and identification, probabilistic and robust engineering design, numerical methods for differential equations, structural health monitoring techniques, power system optimization and stability, and hydraulic and pneumatic systems.

Gugercin has a number of recent publications reflecting these areas of study. Selected papers include:

  • Data-Driven Balancing of Linear Dynamical Systems (2022) in SIAM Journal on Scientific Computing
  • The p-AAA Algorithm for Data-Driven Modeling of Parametric Dynamical Systems (2023) in SIAM Journal on Scientific Computing
  • Estimating dispersion curves from Frequency Response Functions via Vector-Fitting (2020) in Mechanical Systems and Signal Processing
  • Data-Driven Modeling of Linear Dynamical Systems with Quadratic Output in the AAA Framework (2022) in Journal of Scientific Computing
  • A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations (2022) in Mechanical Systems and Signal Processing

Frequent coauthors collaborating with Gugercin include Christopher Beattie, Ion Victor Gosea, Steffen W. R. Werner, A.C. Antoulas, and Petar Mlinarić.

The scientist has published extensively in several academic venues. Significant publication outlets feature:

  • arXiv (Cornell University)
  • SIAM Journal on Scientific Computing
  • Advances in Computational Mathematics
  • Mechanical Systems and Signal Processing
  • Journal of Scientific Computing

In addition to journal articles, Gugercin has contributed to the book literature. One noted book published by the Society for Industrial and Applied Mathematics is titled Interpolatory Methods for Model Reduction (2020).

Best Publications

  • A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

    Peter Benner;Serkan Gugercin;Karen Willcox

  • A survey of model reduction by balanced truncation and some new results

    Serkan Gugercin;Athanasios C. Antoulas

  • A Survey of Model Reduction Methods for Large-Scale Systems

    A.C. Antoulas;D.C. Sorensen;S. Gugercin

  • $\mathcal{H}_2$ Model Reduction for Large-Scale Linear Dynamical Systems

    S. Gugercin;A. C. Antoulas;C. Beattie

  • A New Selection Operator for the Discrete Empirical Interpolation Method---Improved A Priori Error Bound and Extensions

    Zlatko Drmac;Serkan Gugercin

  • Interpolatory Projection Methods for Parameterized Model Reduction

    Ulrike Baur;Christopher Beattie;Peter Benner;Serkan Gugercin

  • Interpolatory Model Reduction of Large-Scale Dynamical Systems

    Athanasios C. Antoulas;Christopher A. Beattie;Serkan Gugercin

  • A modified low-rank Smith method for large-scale Lyapunov equations ∗

    Serkan Gugercin;Danny C. Sorensen;Athanasios C. Antoulas

  • Interpolatory Methods for Model Reduction

    A. C. Antoulas;C. A. Beattie;S. Güğercin

  • An iterative SVD-Krylov based method for model reduction of large-scale dynamical systems

    Serkan Gugercin

  • Interpolatory projection methods for structure-preserving model reduction

    Christopher A. Beattie;Serkan Gugercin

  • Structure-preserving tangential interpolation for model reduction of port-Hamiltonian systems

    Serkan Gugercin;Rostyslav V. Polyuga;Christopher Beattie;Arjan Van Der Schaft

  • Model Reduction of Descriptor Systems by Interpolatory Projection Methods

    Serkan Gugercin;Tatjana Stykel;Sarah Wyatt

  • Multipoint Volterra Series Interpolation and $\mathcal{H}_2$ Optimal Model Reduction of Bilinear Systems

    Garret M. Flagg;Serkan Gugercin

  • Structure-Preserving Model Reduction for Nonlinear Port-Hamiltonian Systems

    Saifon Chaturantabut;Christopher A. Beattie;Serkan Gugercin

  • Quadrature-Based Vector Fitting for Discretized $\mathcal{H}_2$ Approximation

    Zlatko Drmac;Serkan Gugercin;Christopher A. Beattie

  • $\mathcal H_2$-Quasi-Optimal Model Order Reduction for Quadratic-Bilinear Control Systems

    Peter Benner;Pawan Kumar Goyal;Serkan Gugercin

  • A trust region method for optimal H 2 model reduction

    Christopher A. Beattie;Serkan Gugercin

  • Realization-independent ℌ 2 -approximation

    Christopher Beattie;Serkan Gugercin

  • Projection methods for model reduction of large-scale dynamical systems

    Serkan Gugercin

  • Structure-preserving model reduction for nonlinear port-Hamiltonian systems

    Christopher Beattie;Serkan Gugercin

  • Structure-preserving tangential-interpolation based model reduction of port-Hamiltonian systems

    S Gugercin;RV Rostyslav Polyuga;CA Beattie;van der Aj Arjan Schaft

Frequent Co-Authors

Athanasios C. Antoulas
Athanasios C. Antoulas Rice University
Peter Benner
Peter Benner Max Planck Institute for Dynamics of Complex Technical Systems
Karen Willcox
Karen Willcox The University of Texas at Austin
Volker Mehrmann
Volker Mehrmann Technical University of Berlin
Traian Iliescu
Traian Iliescu Virginia Tech
Danny C. Sorensen
Danny C. Sorensen Rice University

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