The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Serkan Gugercin sits on this spectrum.
This scientist: 153 publications — 39th percentile
39% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 537 publications or more.
The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Serkan Gugercin sits on this spectrum.
This scientist: 40 D-Index — 45th percentile
45% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 86 D-Index or more.
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:
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:
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).
Peter Benner;Serkan Gugercin;Karen Willcox
Serkan Gugercin;Athanasios C. Antoulas
A.C. Antoulas;D.C. Sorensen;S. Gugercin
S. Gugercin;A. C. Antoulas;C. Beattie
Zlatko Drmac;Serkan Gugercin
Ulrike Baur;Christopher Beattie;Peter Benner;Serkan Gugercin
Athanasios C. Antoulas;Christopher A. Beattie;Serkan Gugercin
Serkan Gugercin;Danny C. Sorensen;Athanasios C. Antoulas
A. C. Antoulas;C. A. Beattie;S. Güğercin
Serkan Gugercin
Christopher A. Beattie;Serkan Gugercin
Serkan Gugercin;Rostyslav V. Polyuga;Christopher Beattie;Arjan Van Der Schaft
Serkan Gugercin;Tatjana Stykel;Sarah Wyatt
Garret M. Flagg;Serkan Gugercin
Saifon Chaturantabut;Christopher A. Beattie;Serkan Gugercin
Zlatko Drmac;Serkan Gugercin;Christopher A. Beattie
Peter Benner;Pawan Kumar Goyal;Serkan Gugercin
Christopher A. Beattie;Serkan Gugercin
Christopher Beattie;Serkan Gugercin
Serkan Gugercin
Christopher Beattie;Serkan Gugercin
S Gugercin;RV Rostyslav Polyuga;CA Beattie;van der Aj Arjan Schaft
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