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Serkan Gugercin

Publications

Journal Publications

  1. P. Mlinarić, P. Benner, and S. Gugercin. “Interpolatory necessary optimality conditions for reduced-order modeling of parametric linear time-invariant systems“. Submitted, 2024. Available as arXiv:2401.10047
  2. S. Reiter, I.V. Gosea, and S. Gugercin. “Generalization of data-driven balancing:what to sample for different balancing-based reduced models”. Submitted, 2023. Available as arXiv:2312.12561
  3. S. Reiter, T. Damm, M. Embree, and S. Gugercin. “On the balanced truncation error bound and sign parameters from arrowhead realizations.”  Advances in Computational Mathematics, Vol. 50, No:10, 2024. 
  4. M. Ackermann and S. Gugercin. “Frequency-based reduced models from purely time-domain data via data informativity”. Submitted, 2023. Available as arXiv:2311.05012. 
  5. P. Mlinarić, C. Beattie, Z. Drmač, and S. Gugercin. ” IRKA is a Riemannian gradident descent method”. Submitted, 2023. Available as arXiv:2311.02031
  6. A. Carracedo Rodriguez, L. Balicki, and S. Gugercin. “The p-AAA algorithm for data driven modeling of parametric dynamical systems“. SIAM Journal on Scientific Computing, Vol. 45, No: 3, pp. 1332–A1358, 2023.  Preprint version available as arXiv:2003.06536
  7. P. Mlinarić, P. Benner, and S. Gugercin. “H2-optimal reduced-order modeling for structured linear systems“. Submitted, 2023. Available as arXiv:2310.10618
  8. P. Benner, S. Gugercin, and S. Werner. “A unifying framework for tangential interpolation of structured bilinear control systems“. Numerische Mathematik, 2023. Preprint version available as arXiv:2206.01657
  9. P. Benner, S. Gugercin, and S. Werner. “Structured interpolation for multivariate transfer functions of quadratic-bilinear dynamical systems“. Accepted to appear in Advanced in Computational Mathematics, 2024. Available as arXiv:2304.14292
  10. I.V. Gosea, S. Gugercin, and S.W.R. Werner. “Structured barycentric forms for interpolation-based data-driven reduced modeling of second order systems” Available as arXiv:2303.12576
  11. P. Mlinarić and S. Gugercin. “A unifying framework for interpolatory L2-optimal reduced-order modeling.” SIAM Journal on Numerical Analysis, Vol. 61, No. 5, pp. 2133–2156, 2023. Available as arXiv:2209.00714
  12. B. Kramer, S. Gugercin, J. Borggaard, and L. Balicki. “Nonlinear balanced truncation: Part 1 — Computing energy functions.” Available as arXiv:2209.07645.
  13. B. Kramer, S. Gugercin, and J. Borggaard. “Nonlinear balanced truncation: Part 2 –Model reduction on manifolds.” Available as arXiv:2302.02036.
  14. P. Mlinarić and S. Gugercin. “L2-optimal reduced-order modeling using parameter separable forms.“. SIAM Journal on Scientific Computing, Vol. 45, No. 3, pp. A554–A578, 2023 Available as arXiv:2206.02929.
  15. B. Safaee and S. Gugercin.  “Structured-preserving model reduction of nonlinear power grid network.” Submitted, 2022. Available as arXiv:2203.09021
  16. M. Krishnan, S. Gugercin, and P. Tarazaga. “A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations.” Mechanical Systems and Signal Processing, Vol. 187, 109919, 2023. Available as arXiv:2110.12990.
  17. E. Qian, J. Tabeart, C. Beattie, S. Gugercin. J. Jiang, P. Kramer, and  A. Narayan. “Model reduction of linear dynamical systems via balancing for Bayesian inference“, Journal of Scientific Computing,  91(29), 2022.
  18. I. V. Gosea, S. Gugercin, and C. Beattie. “Data-driven balancing of linear dynamical systems.” SIAM Journal on Scientific Computing, Vol. 44, No. 1, pp. A554–A582, 2022
  19. I. V. Gosea and S. Gugercin. “Data-driven modeling of linear dynamical systems with quadratic output in the AAA framework“, Journal of Scientific Computing, 91(16), 2022.
  20. M. Brennan, M. Embree, and S. Gugercin. “Contour integral methods for nonlinear eigenvalue problems: A systems theoretic approach.”  SIAM Review, Vol. 65, No. 2, pp. 439–470, 2023.. 
  21. V.V.N.S. Malladi, M.I. Albakri, M, Krishnan, S. Gugercin, P.A. Tarazaga. “Estimating Experimental Dispersion Curves from Steady-State Frequency Response Measurements“, Mechanical Systems and Signal Processing, Vol. 164, 108218, 2021.. Available as arXiv:2101.00155.
  22. A. Carr, E. de Sturler, and S. Gugercin. “Preconditioning parametrized linear systems”.  SIAM Journal on Scientific Computing, Vol. 43, No. 3, pp. A2242–A2267, 2021. Available as  arXiv:1601.05883.
  23. S. Reiter, M. Embree, and S. Gugercin. “The balanced truncation bound is tight for SISO systems when the truncated system is state-space symmetric”,  2020. Available as arXiv:2011.07170.
  24. P. Benner, S. Gugercin, and S. Werner. “Structure-preserving interpolation for model reduction of parametric bilinear systems“.  Automatica, Vol. 132, 109799, 2021. Available as arXiv:2007.11269
  25. B. Peherstorfer, Z. Drmac, and S. Gugercin. “Stability of discrete empirical interpolation and gappy proper orthogonal decomposition with randomized and deterministic sampling points“.  SIAM Journal on Scientific Computing, Vol. 42, No. 5, pp. A2837–A2864, 2020.
  26. S. Aslan, E. de Sturler, and S. Gugercin. “Randomization for the efficient computation of parametric reduced order models for inversion“. 2020. Available as arXiv:2007.06027
  27. P. Benner, S. Gugercin, and S. Werner. “Structure-preserving interpolation of bilinear control systems.” Accepted to appear in Advances in Computational Mathematics 47, 43, 2021. Available as arXiv:2005.00795
  28. C.A. Beattie, Z. Drmac, and S. Gugercin. “Revisiting IRKA: Connections with pole placement and backward stability.” Vietnam Journal of Mathematics, Vol. 48, pp. 963–985, 2020. Earlier draft version available as arXiv:1911.05804. 
  29. C.A. Beattie, S. Gugercin, and V. Mehrmann. “Structure-preserving Interpolatory Model Reduction for Port-Hamiltonian Differential-Algebraic Systems.” Accepted to appear, 2020. Available as arXiv:1910.05674
  30. T. Breiten, C. Beattie, and S. Gugercin. “H2-gap model reduction for stabilizable and detectable systems.” Accepted to appear, 2020. Available as arXiv:1909.13764
  31. C.A. Beattie, S. Gugercin, and Z. Tomljanovic. “Sampling-free parametric model reduction for structured systems.“.  Advances in Computational Mathematics, 46:83, 2020.  View-only  Sharedit Version. Earlier version as arXiv:1912.11382
  32. M.I. Albakri, V.V.N.S. Malladi, S. Gugercin, P.A. Tarazaga. “Estimating Dispersion Curves from Frequency Response Functions via Vector-Fitting.” Mechanical Systems and Signal Processing, Vol. 140, 106597, 2020.
  33. K. Sinani and S. Gugercin. “H_2(t_f) Optimality Conditions for a Finite-time Horizon“. Automatica, Vol. 110, 2019.
  34. B. Unger and S. Gugercin. “Kolmogorov n-widths for linear dynamical systems“. Advances in Computational Mathematics, Vol. 45, pp. 2273–2286, 2019.
  35. A. Grimm, C.A. Beattie, Z Drmac, and S. Gugercin. “Empirical least-squares fitting of parametrized dynamical systems“. Submitted 2018. Available as arXiv:1808.05716.
  36. P. Benner, S. Gugercin, and K. Willcox. “A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems.” SIAM Review, Vol. 57, Issue: 4, pp. 483—531, 2015.
  37. B. Peherstorfer, S. Gugercin, and K. Willcox. “Data-driven Reduced Model Construction with Time-domain Loewner Models.” SIAM Journal on Scientific Computing, Vol. 39, Issue 5, pp. A2152—A2178, 2017.
  38. V. Malladi, M. Albakri, S. Gugercin, P. Tarazaga. “Application of projection-based model reduction to finite-element plate models for two-dimensional traveling waves.” Journal of Intelligent Material Systems and Structures, Vol 28, Issue 14, pp. 1886—1904, 2017 .
  39. A. Carracedo Rodriguez, S. Gugercin, and J. Borggaard. “Interpolatory Model Reduction of Parameterized Bilinear Dynamical Systems“. Advances in Computational Mathematics, pp. 1—30, 2018.
  40. Z. Tomljanovic, C.A. Beattie, and S. Gugercin. “Damping optimization of parameter dependent mechanical systems by rational interpolation“. Advances in Computational Mathematics, pp. 1—24, 2018.
  41. P. Schulze, B. Unger, C. Beattie, and S Gugercin.”Data-driven Structured Realization“. Linear Algebra and Its Applications, Volume 537, Issue 15, pp. 250—286, 2018.
  42. P. Benner, P. Goyal, and S. Gugercin. “H2 -Quasi-Optimal Model Order Reduction for Quadratic-Bilinear Control Systems.” SIAM Journal on Matrix Analysis and Applications, Vol. 39 No. 2, pp. 983—1032, 2018.
  43. C. Beattie, S. Gugercin and V. Mehrmann. “ Model Reduction for Systems with Inhomogeneous Initial Conditions”. Systems and Control Letters, Vol. 99, pp. 99—106, 2017.
  44. M. O’Connell, M. Kilmer, E. de Sturler, and S. Gugercin. “Computing Reduced Order Models via Inner-Outer Krylov Recycling in Diffuse Optical Tomography”. SIAM Journal on Scientific Computing, Vol. 39, No. 2, pp. B272—B297, 2017.
  45. C. Magruder, C. Beattie, S. Gugercin. “Linear time-periodic dynamical systems: An H2 analysis and a model reduction framework.” Mathematical and Computer Modelling of Dynamical Systems, pp. 1—24, 2017.
  46. S. Chaturantabut, C. Beattie, and S. Gugercin. “Structure-preserving Model Reduction for Nonlinear Port-Hamiltonian Systems“. SIAM Journal on Scientific Computing, Vol. 38, No. 5, pp. B837—B865, 2016.
  47. Z. Drmac and S. Gugercin. “A New Selection Operator for the Discrete Empirical Interpolation Method — improved a priori error bound and extensions.” SIAM Journal on Scientific Computing, Vol. 38, No. 2, pp. A631—A648, 2016.
  48. B. Kramer and S. Gugercin. ” The Eigensystem Realization Algorithm from Tangentially Interpolated Data”. Mathematical and Computer Modelling of Dynamical Systems, Vol. 22, Issue. 4, pp. 282—306, 2016.
  49. Z. Drmac, S. Gugercin and C.A. Beattie. “Vector Fitting for Matrix-valued Rational Approximation.”  SIAM Journal on Scientific Computing, Vol. 37, Issue: 5, pp. A2151—S626, 2015.
  50. Z. Drmac, S. Gugercin and C.A. Beattie. “Quadrature-Based Vector Fitting For Discretized H2 Approximation.” SIAM Journal on Scientific Computing, Vol. 37, Issue: 2, A625—A652, 2015.
  51. G. Flagg and S. Gugercin. “Multipoint Volterra Series Interpolation and H2 Optimal Model Reduction of Bilinear Systems.” SIAM Journal on Matrix Analysis and Applications, Vol. 36, Issue: 2, 549—579, 2015.
  52. E. de Sturler, S. Gugercin, M. E. Kilmer, S. Chaturantabut, C. Beattie, and M. O’Connell. “Nonlinear Parametric Inversion using Interpolatory Model Reduction.” SIAM Journal on Scientific Computing, Vol. 37, Issue: 3, B495—B517, 2015.
  53. T. Breiten, C. Beattie, and S. Gugercin. “Near-optimal Frequency-weighted Interpolatory Model Reduction.” Systems and Control Letters. Vol. 78, pp. 8—18.
  54. S. Gugercin, T. Stykel, and S. Wyatt. “Model Reduction of Descriptor Systems by Interpolatory Projections Methods.” SIAM Journal on Scientific Computing, Vol. 35, Iss. 5, pp. B1010—B1033, 2013.
  55. G. Flagg and S. Gugercin. “On the ADI method for the Sylvester Equation and the optimal-H2 points.” Applied Numerical Mathematics, Vol. 64, pp. 50—58, 2013.
  56. B. Anic, C.A. Beattie, S. Gugercin and A.C. Antoulas. “Interpolatory Weighted-H2 Model Reduction.” Automatica, Vol. 49, Iss. 5, pp. 1275—1280, 2013.
  57. G. Flagg, C.A. Beattie and S. Gugercin, “Convergence of the Iterative Rational Krylov Algorithm“, Systems and Control Letters, Volume: 61, Issue: 6, pp. 688—691, 2012.
  58. G. Flagg, C.A. Beattie and S. Gugercin. “Interpolatory H-infinity Model Reduction,” Systems and Control Letters, Vol. 62, Iss. 7, pp. 567—574, 2013.
  59. K. Ahuja, E. de Sturler, R. Chang and S. Gugercin, ” Recycling BiCG with an Application to Model Reduction”, SIAM Journal on Scientific Computing, Vol. 34, Issue: 4, pp. A1925—A949, 2012.
  60. S. Gugercin, R. V. Polyuga, C.A. Beattie and A. van der Schaft, “Structure-preserving tangential-interpolation based model reduction of port-Hamiltonian Systems.” Automatica, Vol. 48, Issue: 9, pp. 1963—1974, 2012.
  61. C.A. Beattie, S. Gugercin, and S. Wyatt, “Inexact Solves in Interpolatory Model Reduction”. Linear Algebra and its Applications, Vol. 436, Issue: 8, pp. 2916—2943, 2012.
  62. C.A. Beattie, Z. Drmac, and S. Gugercin, A note on shifted Hessenberg systems and frequency response computation, ACM Transaction on Mathematical Software, Vol 38, No. 2, 2011.
  63. U. Baur, C.A. Beattie, P. Benner and S. Gugercin, “Interpolatory Projection Methods for Parameterized Model Reduction”. SIAM Journal on Scientific Computing, Vol. 33, Issue: 5, pp. 2489—2518, 2011.
  64. C.A. Beattie and S. Gugercin, “Interpolatory Projection Methods for Structure-preserving Model Reduction”. Systems and Control Letters, Vol. 58, Issue: 3, pp: 225—232, 2009.
  65. S. Gugercin, A.C. Antoulas and C.A. Beattie, “H2 model reduction for large-scale linear dynamical systems”. SIAM Journal on Matrix Analysis and Applications Vol. 30, Issue: 2, pp. 609—638, (2008).
  66. S. Gugercin, “An iterative SVD-Krylov based algorithm for model reduction of large-scale dynamical systems“, Linear Algebra and its Applications Vol. 428 No: 8—9 pp. 1964-1986 (2008).
  67. S. Gugercin and K. Willcox, “Krylov projection framework for Fourier model reduction“, Automatica Vol. 44 No:1 pp. 209—215 (2008) .
  68. S. Gugercin and A.C. Antoulas. “Model reduction of large-scale systems by least squares“, Linear Algebra and its Applications, Special Issue on Order Reduction of Large-Scale Systems, Vol. Vol: 415 No:2-3, pp. 290—321 (2006).
  69. S. Gugercin and A.C. Antoulas. A survey of model reduction by balanced truncation and some new results. International Journal of Control, Vol: 77 No: 8, pp. 748—766 (2004) (Copyright Taylor & Francis, 2004)
  70. S. Gugercin, A.C. Antoulas, and H.P. Zhang. “An approach to identification for robust control“, IEEE Transactions on Automatic Control, Vol. 48 No: 6 pp. 1109—1115 (2003).
  71. S. Gugercin, D.C. Sorensen, and A.C. Antoulas. “A modified low-rank Smith method for large-scale Lyapunov Equations“,  Numerical Algorithms, Vol. 32, No: 1, pp. 27—55, (2003)
  72. A.C. Antoulas, D.C. Sorensen, and S. Gugercin. A survey of model reduction methods for large-scale systems. Structured Matrices in Operator Theory, Numerical Analysis, Control, Signal and Image Processing, Contemporary Mathematics, AMS publications, 280: 193—219, 2001. (Copyright AMS, 2001)

Books / Book Chapters

  1. A.C. Antoulas, C.A. Beattie, and S. Gugercin. Interpolatory Methods for Model Reduction. Computational Science and Engineering 21, SIAM, Philadelphia, 2020.
  2. A. Castagnotto, C.A. Beattie, and S. Gugercin. Interpolatory methods for H∞ model reduction of multi-input/multi-output systems. In: Benner P., Ohlberger M., Patera A., Rozza G., Urban K. (eds) Model Reduction of Parametrized Systems. MS&A (Modeling, Simulation and Applications), vol 17, pp. 349—365, Springer, Cham, 2017.
  3. J.T. Borggaard and S. Gugercin. Model Reduction for DAEs with an Application to Flow Control. Active Flow and Combustion Control 2014, R. King editors, Springer-Verlag, Notes on Numerical Fluid Mechanics and Multidisciplinary Design, Vol. 127, (ISBN 978-3-319-11966-3), pp. 381—396, 2015.
  4. C.A. Beattie and S. Gugercin. Model Reduction by Rational Interpolation. In P. Benner, A. Cohen, M. Ohlberger, and K. Willcox (eds.), Model Reduction and Approximation: Theory and Algorithms, pp. 297—334, SIAM, Philadelphia, PA, 2017.
  5. A.C. Antoulas, C.A. Beattie and S. Gugercin. Interpolatory model reduction of large-scale dynamical systems. Efficient Modeling and Control of Large-Scale Systems, J. Mohammadpour and K. Grigoriadis editors, Springer-Verlag, ISBN 978-1-4419-5756-6, Publication date: Feb. 2010. (Copyright Springer, 2009)
  6. S. Gugercin and J.-R. Li. Smith-type methods for balanced truncation of large-sparse systems. Dimension Reduction of Large-scale Systems, P. Benner, G.H. Golub, V.L. Mehrman and D.C. Sorensen editors, Springer-Verlag, Lecture Notes in Computational Science and Engineering, Vol. 45 (ISBN 3-540-24545-6), Berlin/Heidelberg, 2005. (Copyright Springer, 2005)

Refereed Conference Proceedings

  1. N. Shyamkumar, S. Gugercin, and B. Peherstorfer. “Towards context-aware learning for control: Balancing stability and model-learning error.” Accepted to appear in Proceedings of the 2022 American Control Conference, 2022. Preprint available here.
  2. S. W. R. Werner, I. V. Gosea, and S. Gugercin. “Structured vector fitting framework for mechanical systems.” Submitted, 2021. Available as arXiv:2110.09220
  3. I. V. Gosea, S. Gugercin, and B. Unger. “Parametric model reduction via rational interpolation along parameters.”  IEEE Conference on Decision and Control, 2022. Available as arXiv:2104.01016.
  4. B. Safaee and S. Gugercin. “Data-driven modeling of power networks.” IEEE Conference on Decision and Control, 2021. Available as arXiv:2104.06478.
  5. B. Safaee and S. Gugercin. “Structure-preserving model reduction of parametric power networks.” Proceedings of the 2021 American Control Conference Submitted, 2021. Available as arXiv:2102.05179.
  6. I. V. Gosea and S. Gugercin. “The AAA Framework for Modeling Linear Dynamical Systems with Quadratic Output.” Extended Abstract,  21st IFAC World Congress (IFAC 2020). Available as arXiv:2005.10316.
  7. K. Sinani, S. Gugercin, and C. Beattie. ” A Structure-preserving Model Reduction Algorithm for Dynamical Systems with Nonlinear Frequency Dependence” . Proceedings of the 6th IFAC Symposium on System Structure and Control SSSC 2016. Available as IFAC-PapersOnLine, Volume 49, Issue 9, 2016, Pages 56-61.
  8. I. Pontes Duff, S. Gugercin, C. Beattie, C. Poussot-Vassal, C. Seren. “H2-optimality conditions for reduced time-delay systems of dimension one”. Proceedings of the 13th IFAC Workshop on Time Delay Systems TDS, 2016. Available as IFAC-PapersOnLine Volume 49, Issue 10, 2016, Pages 7–12.
  9. J. Borggaard, S. Gugercin, and Lizette Zietsman. Feedback Stabilization of Fluids Using Interpolatory and POD Reduced-Order Models for Control and Compensator Design Accepted to appear in Proceedings of the 55th IEEE Conference on Decision and Control, 2016.
  10. C.A. Beattie, Z. Drmac, S. Gugercin. “Quadrature-Based IRKA Fitting for optimal H2 approximation.”Proceedings of 8th Vienna International Conferenceon Mathematical Modelling – MATHMOD 2015, available as IFAC-PapersOnLine, Volume 48, Issue 1, Pages 5-6, 2015.
  11. J. Borggaard, S. Gugercin, and L. Zietsman. “Compensators via H2-based Model Reduction and Proper Orthogonal Decomposition” Proceedings of 19th IFAC World Congress, 2014.
  12. C.A. Beattie, and S. Gugercin. Realization-independent H2-approximation. Proceedings of the 51st IEEE Conference on Decision and Control, Maui, HI, USA, December 2012. (Copyright IEEE, 2012)
  13. D. Kim, J. Braun, J. Borggaard, E. Cliff, S. Gugercin. Coupled CFD/Building Envelope Model for the Purdue Living Lab. Proceeding of the 2012 High Performance Buildings Conference at Purdue, West Lafayette, IN, USA, July 2012.
  14. J. Borggaard, E. Cliff and S. Gugercin. Model reduction for indoor-air behavior in control design for energy-efficient Buildings. Proceedings of the 2012 American Control Conference, Montreal, Canada, June 2012.
  15. C.A. Beattie, and S. Gugercin. Structure-preserving model reduction of nonlinear port-Hamiltonian systems. Proceedings of the 50th IEEE Conference on Decision and Control, pp. 6564-6569, Orlando, FA, USA, 2011.
  16. C.A. Beattie and S. Gugercin. Weighted model reduction via interpolation. Proceedings of the 18th IFAC World Congress, pp. 12757-12760, Milano, Italy, August 28- September 2, 2011.
  17. C. Magruder, C.A. Beattie, and S. Gugercin. Rational Krylov methods for optimal L2 model reduction. Proceedings of the 49th IEEE Conference on Decision and Control, pp. 6797-6802, Atlanta, GA, USA, December 2010. (Copyright IEEE, 2010)
  18. G. Flagg, S. Gugercin and C.A. Beattie. An interpolation-based approach to H∞ model reduction of dynamical systems. Proceedings of the 49th IEEE Conference on Decision and Control, pp. 6791-6796, Atlanta, GA, USA, December 2010. (Copyright IEEE, 2010)
  19. C.A. Beattie and S. Gugercin. A trust region method for optimal H2 model reduction. Proceedings of the Joint 48th IEEE Conference on Decision and Control, and 28th Chinese Control Conference, pp. 5370-5375, Shanghai, P.R. China, December 2009. (Copyright IEEE, 2009)
  20. S. Gugercin, R. Polyuga, C.A. Beattie and, A. van der Schaft. Interpolation-based H2 model reduction for port-Hamiltonian systems. Proceedings of the Joint 48th IEEE Conference on Decision and Control, and 28th Chinese Control Conference, pp. 5362-5369, Shanghai, P.R. China, December 2009. (Copyright IEEE, 2009)
  21. C.A. Beattie and S. Gugercin. Interpolation theory for structure-preserving model reduction. Proceedings of the 47th IEEE Conference on Decision and Control, pp. 4204-4208, Cancun, Mexico, December 2008. (Copyright IEEE, 2008)
  22. C.A. Beattie and S. Gugercin. Krylov-based minimization for optimal H2 model reduction. Proceedings of the 46th IEEE Conference on Decision and Control, 2007. (Copyright IEEE, 2007)
  23. S. Gugercin, A. C. Antoulas and Christopher A. Beattie. A rational Krylov Iteration for Optimal H2   Model Reduction. Proceedings of the 17th International Symposium on Mathematical Theory of Networks and Systems, pp. 1665-1667, July 2006.
  24. C.A. Beattie and S. Gugercin. Inexact solves in Krylov-based model reduction. Proceedings of the 45th IEEE Conference on Decision and Control, pp. 3405-3411, December 2006. (Copyright IEEE, 2006)
  25. C.A. Beattie, J. Borggaard, S. Gugercin and T. Iliescu. A domain decomposition approach to POD. Proceedings of the 45th IEEE Conference on Decision and Control, pp. 6750-6756, December 2006. (Copyright IEEE, 2006)
  26. C.A. Beattie and S. Gugercin. Krylov-based model reduction of second-order systems with proportional damping. Proceedings of the 44th IEEE Conference on Decision and Control, pp. 2278-2283, December 2005. (Copyright IEEE, 2005)
  27. S. Gugercin. An iterative SVD-Krylov based method for model reduction of large-scale dynamical systems.  Proceedings of the 44th IEEE Conference on Decision and Control, pp. 5905-5910, December 2005. (Copyright IEEE, 2005)
  28. S. Gugercin, A.C. Antoulas, C.A. Beattie, and E. Gildin. Krylov-based controller reduction for large-scale systems. Proceedings of the 43rd IEEE Conference on Decision and Control, Vol. 3, pp. 3074-3077, December 2004. (Copyright IEEE, 2004)
  29. C.A. Beattie, S. Gugercin, A.C. Antoulas and E. Gildin. Controller reduction by Krylov projection methods. Proceedings of the 16th International Symposium on Mathematical Theory of Networks and Systems, July 2004.
  30. S. Gugercin and A.C. Antoulas. An H2 error expression for the Lanczos procedure. Proceedings of the 42nd IEEE Conference on Decision and Control, Vol. 2, 1869-1872, December 2003. (Copyright IEEE, 2003)
  31. S. Gugercin and A.C. Antoulas. A time-limited balanced reduction method. Proceedings of the 42nd IEEE Conference on Decision and Control, Vol. 5, pp. 5250-5253, December 2003. (Copyright IEEE, 2003)
  32. S. Gugercin and A.C. Antoulas. A survey of balancing methods for model reduction.  Proceedings of European Control Conference 2003, Cambridge, UK,September 2003.
  33. A.C. Antoulas and S. Gugercin. A new approach to model reduction which preserves stability and passivity. Proceedings of the 41st IEEE Conference on Decision and Control, Vol. 3, pp. 2544-2545, Las Vegas, Nevada, December 2002. (Copyright IEEE, 2002)
  34. S. Gugercin, A.C. Antoulas, N. Bedrossian. Approximation of International Space Station 1R and 12A Models. Proceedings of the 40th IEEE Conference on Decision and Control, Vol. 3, pp. 1515-1516, Orlando, Florida, December 2001. (Copyright IEEE, 2001)
  35. S. Gugercin and A.C. Antoulas. A comparative study of 7 algorithms for model reduction. Proceedings of the 39th IEEE Conference on Decision and Control, Vol. 3, pp. 2367-2372, Sydney, Australia, December 2000. (Copyright IEEE, 2000)
  36. S. Gugercin and A.C. Antoulas. On consistency and model validation for systems with parameter uncertainty. Proceedings of SYSID2000, Santa Barbara, California, June 2000. (Copyright IFAC, 2000)
  37. S. Gugercin and A.C. Antoulas. On the assignment of eigenvalues in LTI systems. Proceedings of the 38th IEEE Conference on Decision and Control, Vol. 1, pp. 486, Phoenix, Arizona, December 1999. (Copyright IEEE, 1999)