Proper orthogonal decomposition for optimality systems

By Karl Kunisch, Stefan Volkwein. 2008

199 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9689305982316f973f5802fc67f0a67fa4696221

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Alessandro Astolfi
2. Charbel Farhat
3. Kevin T. Carlberg
4. Asven Gariah
5. Jacques Sainte-Marie
6. Dominique Chapelle
7. Martin Gubisch
8. Danny C. Sorensen

In Statistical and Nonlinear Physics

1. Charbel Farhat
2. Saifon Chaturantabut
3. Danny C. Sorensen
4. Kevin T. Carlberg
5. Asven Gariah
6. Jacques Sainte-Marie
7. Dominique Chapelle
8. Martin Gubisch

In Statistics and Probability

1. Charbel Farhat
2. Danny C. Sorensen
3. Saifon Chaturantabut
4. Kevin T. Carlberg
5. Martin Gubisch
6. Alessandro Astolfi
7. Asven Gariah
8. Jacques Sainte-Marie

Citations in the corpus

• Model Reduction by Moment Matching for Linear and Nonlinear Systems (2010, methodology): “Finally, proper orthogonal decomposition has been proposed as a model reduction method applicable for linear and nonlinear systems, see, for example, [43], [44], [42], [45], [46], where theoretical issues, error bounds and applications have been discussed.”
• Nonlinear Model Reduction via Discrete Empirical Interpolation (2010, background): “Kunisch and Volkwein [51] suggested a way to avoid the dependence on the choice of the snapshots in optimal control applications.”
• Galerkin approximation with proper orthogonal decomposition : new error estimates and illustrative examples (2012, methodology): “The reduced basis [20, 21, 23, 24, 27] and the proper orthogonal decomposition (POD) [12,15, 17, 19, 28] are two techniques belonging to this second family.”
• 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 for Linear-Quadratic Optimal Control (2013, background): “In this case a change of the POD basis is needed; see, [43, 76], for instance.”
• A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, unknown): “Another approach for optimal control incorporates the optimality conditions into derivation of the reduced model [144].”