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State-of-the-Art Model Order Reduction and Its Applications in 2020

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

  1. The hyper-reduction problem for reduced-order internal forces evaluation in transient, nonlinear, explicit dynamics is reformulated, employing Mixed-Integer Programming (MIP), taking into account consistency c...

    Authors: Pierre Phalippou, Piotr Breitkopf, Salim Bouabdallah, Malek Zarroug and Pierre Villon

    Citation: Advanced Modeling and Simulation in Engineering Sciences 2020 7:36

    Content type: Research article

    Published on:

  2. In this work, we consider a transient thermal problem, with a nonlinear term coming from the radiation boundary condition and a nonparametrized variability in the form complex scenarios for the initial conditi...

    Authors: Fabien Casenave, Asven Gariah, Christian Rey and Frederic Feyel

    Citation: Advanced Modeling and Simulation in Engineering Sciences 2020 7:22

    Content type: Research article

    Published on: