- David Néron (ENS Paris-Saclay, Gif-sur-Yvette, France)
- Elias Cueto Elias Cueto (University of Zaragoza, Zaragoza, Spain)
- Yvon Maday (Université Pierre et Marie Curie, Paris, France)
- Gianluigi Rozza (International School for Advanced Studies, Trieste, Italy)
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.
Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies, including those pertaining to Collections. Articles will be added to the Collection as they are published