Approximation of Large-Scale Dynamical Systems (Advances in Design and Control) (Advances in Design and Control)

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Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition (2009, methodology): “…popular approaches uses the proper orthogonal decomposition (POD) (Loève 1955) of simulation time snapshots at a given set of the parameters (see, for example, Sirovich 1987; Peterson 1989; Holmes, Lumley & Berkooz 1996; Sirisup & Karniadakis 2004; Antoulas 2005; Couplet, Basdevant & Sagaut 2005).”
Comparison of the reduced-basis and POD a posteriori error estimators for an elliptic linear-quadratic optimal control problem (2011, unknown): “Some reduced order methods like balanced truncation offer a reliable a-priori error analysis for optimal control applications, [1, 2].”
A New Technique For Reduced-Order Modelling of Linear Time-Invariant System (2017, methodology): “Furthermore, plenty of time domain order-reduction methods are available in the literature such as proper orthogonal decomposition [16] and Krylov subspace techniques [17] proposed for reduced-order modelling of large-scale dynamical systems.”
When Inequality Matters for Macro and Macro Matters for Inequality (2018, unknown): “All lecture notes for Amsallem and Farhat (2011) are available online at https://web.stanford.edu/group/frg/course_work/CME345/ and the book by Antoulas (2005) is available for free at http://epubs.siam.org/doi/book/10.1137/1.9780898718713.”