Model Order Reduction is experiencing continuous advancements towards increased efficiency and robustness, as well as the embracement of challenging applications with scientific and technological relevance. This collection is intended to group together papers of recent advanced techniques, pushing forward the limits of the current understanding in model order reduction techniques in engineering sciences and mathematics.
Topics of relevance include but are not limited to, Reduced Basis (RB), Proper Orthogonal Decomposition (POD) and Proper Generalized Decomposition (PGD) methods for the numerical solution of models involving partial differential equations. Many methods are now mature and it is time to review their resulting applications. New opportunities and techniques related to big data will also be investigated.
Guest Editors: David Néron, Elias Cueto, Yvon Maday, Gianluigi Rozza