The Loewner framework and transfer functions of singular/rectangular systems. A connection is established between the Loewner framework for model reduction and the generalized inverses of singular and rectangular matrices. In this context both the Moore-Penrose and the Drazin inverses are involved. As a consequence this approach yields transfer functions for singular and rectangular systems. Thus the Loewner framework constitutes a natural and direct way for constructing models from measured input/output data.

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  1. Benner, Peter; Goyal, Pawan: Interpolation-based model order reduction for polynomial systems (2021)
  2. Benner, Peter; Gugercin, Serkan; Werner, Steffen W. R.: Structure-preserving interpolation of bilinear control systems (2021)
  3. Brevis, Ignacio; Muga, Ignacio; van der Zee, Kristoffer G.: A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations (2021)
  4. Gosea, Ion Victor; Güttel, Stefan: Algorithms for the rational approximation of matrix-valued functions (2021)
  5. Haasdonk, Bernard: MOR software (2021)
  6. Uy, Wayne Isaac Tan; Peherstorfer, Benjamin: Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations (2021)
  7. Uy, Wayne Isaac Tan; Peherstorfer, Benjamin: Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories (2021)
  8. Antoulas, Athanasios C.; Gosea, Ion Victor; Heinkenschloss, Matthias: Data-driven model reduction for a class of semi-explicit DAEs using the Loewner framework (2020)
  9. Beattie, Christopher; Gugercin, Serkan; Tomljanović, Zoran: Sampling-free model reduction of systems with low-rank parameterization (2020)
  10. Benner, Peter; Goyal, Pawan; Kramer, Boris; Peherstorfer, Benjamin; Willcox, Karen: Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms (2020)
  11. Borcea, L.; Druskin, V.; Mamonov, A.; Moskow, S.; Zaslavsky, M.: Reduced order models for spectral domain inversion: embedding into the continuous problem and generation of internal data (2020)
  12. Gosea, Ion Victor; Duff, Igor Pontes; Benner, Peter; Antoulas, Athanasios C.: Model order reduction of switched linear systems with constrained switching (2020)
  13. Gosea, Ion Victor; Zhang, Qiang; Antoulas, Athanasios C.: Preserving the DAE structure in the Loewner model reduction and identification framework (2020)
  14. Hijazi, Saddam; Stabile, Giovanni; Mola, Andrea; Rozza, Gianluigi: Data-driven POD-Galerkin reduced order model for turbulent flows (2020)
  15. Kergus, Pauline; Demourant, Fabrice; Poussot-Vassal, Charles: Identification of parametric models in the frequency-domain through the subspace framework under LMI constraints (2020)
  16. Nakatsukasa, Yuji; Trefethen, Lloyd N.: An algorithm for real and complex rational minimax approximation (2020)
  17. Peherstorfer, Benjamin: Sampling low-dimensional Markovian dynamics for preasymptotically recovering reduced models from data with operator inference (2020)
  18. Pradovera, Davide: Interpolatory rational model order reduction of parametric problems lacking uniform inf-sup stability (2020)
  19. Regazzoni, F.; Dedè, L.; Quarteroni, A.: Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics (2020)
  20. Scarciotti, Giordano; Jiang, Zhong-Ping; Astolfi, Alessandro: Data-driven constrained optimal model reduction (2020)

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