Trust-region proper orthogonal decomposition for flow control
By E. Arian, Marco Fahl, E. Sachs. 2000
164 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/9c8240c5ed712ffaf910b326329b4ec1fa20d782
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Citations in the corpus
- Recursive Trust-Region Methods for Multiscale Nonlinear Optimization (2008, background): “More generally, the algorithm and theory presented here are relevant in most areas where simplified models are considered, such as multidisciplinary optimization [1, 2, 3] or PDE-constrained problems [4, 14].”
- Galerkin proper orthogonal decomposition methods for parabolic problems (2001, methodology): “On a computational level progress was made on this topic in the context of optimal control of the Navier–Stokes equation where a dynamic update of the basis elements was performed, see [1, 2 , 10].”
- A low‐cost, goal‐oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems (2011, methodology): “These methods, which include Trust Region POD [16], POD for Optimality Systems [17], and others [18, 19, 44], update or replace the reduced basis as the optimization algorithm progresses.”
- Proper Orthogonal Decomposition Surrogate Models for Nonlinear Dynamical Systems: Error Estimates and Suboptimal Control (2005, unknown): “It was successfully used in a variety of fields including signal analysis and pattern recognition (see [Fuk90]), fluid dynamics and coherent structures (see [AHLS88, HLB96, NAMTT03, RF94, Sir87]) and more recently in control theory (see [AH01, AFS00 , LT01, SK98, TGP99]) and inverse problems (see [”
- Proper orthogonal decomposition for optimality systems (2008, methodology): “We iterated this strategy until the suboptimal controls converge within a given tolerance; see also [1, 2].”
- Proper Orthogonal Decomposition for Linear-Quadratic Optimal Control (2013, methodology): “Proper orthogonal decomposition, model-order reduction, apriori and a-posteriori error analysis, linear-quadratic optimal control, primal-dual active set strategy.”