An efficient computational framework for reduced basis approximation and a posteriori error estimation of parametrized Navier–Stokes flows
By Andrea Manzoni. 2014
50 citations recorded by the source corpus.
Subfields: Computational Mechanics, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/5cde8d20f9ac58d241c630fc19ad662bf3ff6a6c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Andrea Manzoni
- Gianluigi Rozza
- Alfio Maria Quarteroni
- Claudio Paniconi
- Mario Putti
- Francesco Ballarin
- Jan S. Hesthaven
- Paolo Pacciarini
In Statistical and Nonlinear Physics
- Andrea Manzoni
- Gianluigi Rozza
- Alfio Maria Quarteroni
- Claudio Paniconi
- Mario Putti
- Francesco Ballarin
- Stefano Lorenzi
- Paolo Pacciarini
Citations in the corpus
- Supremizer stabilization of POD–Galerkin approximation of parametrized steady incompressible Navier–Stokes equations (2015, methodology): “The chance to apply the supremizer enrichment also within a POD context, shown in this work, makes POD a viable alternative to greedy RB methods in order to recover pressure fields whenever (i) an accurate and rapid error bound is not available or (ii) lower bound to parametrized stability factors i”
- Physically based modeling in catchment hydrology at 50: Survey and outlook (2015, methodology): “Recent research in this field has been spurred by advances in proper orthogonal decomposition and reduced basis finite element methods [Kunisch and Volkwein, 2001; Vermeulen et al., 2004; Grepl and Patera, 2005; Bui-Thanh et al., 2008; Quarteroni et al., 2011; Manzoni, 2014].”