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Serkan Gugercin

Research

  • National Science Foundation Grant DCSD-2130695, Collaborative Research: Nonlienar Balancing: Reduced Models and Control, Co-PIs: J. Borggaard, and S. Gugercin. January 1, 2022 – December 31, 2024, VT Budget $469,799. (Collaborative PI: B. Kramer at UCSD).
  • Rolls-Royce Corporation (Task Order #21-VT-20), PO5100003511, Data-driven modeling of aero engine vibrational response, CO-PIs: P. Tarazaga and S. Gugercin, January 1 – December 2021, $12,270.
  • Rolls-Royce Corporation (Task Order #20-VT-20), PT35UGSH, Data-driven modeling of aero engine vibrational response, CO-PIs: P. Tarazaga and S. Gugercin, July – December 2020, $20,000.
  • National Science Foundation Grant DMS-1923221, AMPS: Model Reduction for Analysis, Identification, and Optimal Design of Power Networks, Co-PIs: C.A. Beattie, M. Embree, and S. Gugercin, and V. Kekatos, August 1, 2019 – July 31, 2022, $376,481.
  • National Science Foundation Grant DMS- 1819110, Efficient Algorithms for Optimal Control of Time-Periodic and Nonlinear Systems, Co-PIs: C.A. Beattie, J. Borggaard, and S. Gugercin, August 01, 2018 – July 31, 2021, $279,913.
  • Croatian Science Foundation Grant CSF IP-2019-04-6774, Vibration Reduction in Mechanical Systems, Co-PIs: S. Gugercin, I. Ivicic, S. Miodragovic, Z. Tomjlanovic. January 1, 2020 December 31, 2023, $129,777 (867,500 Croatian Kuna)
  • Croatian Science Foundation Grant CSF IP-2019-04-6268, {emph Randomized low rank algorithms and applications to parameter dependent problems}, Co-PIs: Z. Drmac, Z. Bujanovic, N. Bosner, I. Glibic, L. Perisa, D. Kressner, S. Gugercin, J. Oval, H. Hakula. January 15, 2020 – January 14, 2024, $96,176 (641,569 Croatian Kuna).
  • National Science Foundation Grant DMS-1720257, Algorithms for Large-Scale Nonlinear Eigenvalue Problems: Interpolation, Stability, Transient Dynamics, Co-PIs: M. Embree and S. Gugercin, July 01, 2017 – July 30, 2020, $349,999.
  • National Science Foundation Grant DMS-1522616, Interpolatory Model Reduction for the Control of Fluids, Co-PIs: J. Borggaard and S. Gugercin, July 15, 2015 – July 14, 2018, $319,933.
  • National Institute for Occupational Safety and Health, Investigation of Reduced Order Fire Modeling for Improved Safety and Response in Underground Coal Mines. Co-PIs: J. Borggard, S. Gugercin, B. Lattimer, K. Luxbacher, S. Schafrik. September 2014- August 2019, $1,247,839.
  • National Science Foundation Grant DMS- 1217156, Collaborative Research: Innovative Integrative Strategies for Nonlinear Parametric Inversion. Co-PIs: C. Beattie, E. de Sturler, S. Gugercin, and M. Kilmer. September 2012- August 2015, $359,942 (Virginia Tech component), $190,00 (Tufts component).
  • Department of Energy, Advanced Computer Design Tools for Modeling, Design, Control, Optimization and Sensitivity Analysis of Integrated Whole Building Systems (VT ICAM component of the Greater Philadelphia Innovation Cluster, DOE HUB). Co-PIs: J. Burns, E. Cliff, S. Gugercin, T. Herdman, T. Iliescu, M. Marathe and L. Zietsman, 2010-2015, $1,463,459.
  • National Science Foundation Grant DMS- 0645347, CAREER: Reduced-order Modeling and Controller Design for Large-scale Dynamical Systems via Rational Krylov Methods, May 1, 2007 – April 30, 2013, $400,000.
  • Air Force Office of Scientific Research Grant FA9550-05-1-0449, High Performance Parallel Algorithms for Improved Reduced-Order Modeling, Co-PIs: J. Borggaard, C. Beattie, S. Gugercin, and T. Iliescu, August 15, 2005 – November 30, 2007, $502,245.
  • National Science Foundation Grant DMS-0513542, Computation and Analysis of Reduced-Order Models for Distributed Parameter Systems, Co-PIs: J. Borggaard, C. Beattie, S. Gugercin, and T. Iliescu, June 15, 2005 – June 14, 2008, $431,342.
  • National Science Foundation Grant DMS-0505971, Model Reduction with Rational Krylov Methods, Co-PIs: C. Beattie and S. Gugercin, June 1, 2005 – May 31, 2008, $210,766.

Research Statement

My research lies in the area of model reduction, whose primary goal is to replace large-scale dynamical systems with lower dimensional dynamical systems having as near as possible the same input/output response characteristics as the original.

In contexts where the input/output characteristics are primary, the resulting reduced model can then be used to replace the original model, potentially as a far more efficient component within a larger simulation.

This research area is highly interdisciplinary in character and has a large overlap with other areas of mathematics and scientific computing, such as numerical analysis, systems and control theory, and optimization. It has found immediate application in diverse areas of the physical sciences and engineering including signal propagation and interference in electric circuits; (PDE) constrained optimization; uncertainty quantification and inverse problems.  I have focussed on both theoretical and computational aspects of model reduction as well as their application in real-world problems.

However, the methods and theory I develop are not specific to a particular application; rather they apply to a wide range of problems.