Computational Reduction for Parametrized PDEs: Strategies and Applications
By Andrea Manzoni, Alfio Maria Quarteroni, Gianluigi Rozza. 2012
50 citations recorded by the source corpus.
Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f4b13a4dd0d6ac839a2124820891ba2d3c29266a
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
- Stefano Lorenzi
- L. Luzzi
- Antonio Cammi
- Gianluigi Rozza
- Andrea Manzoni
- Alfio Maria Quarteroni
- Anthony T. Patera
- Paolo Pacciarini
In Statistical and Nonlinear Physics
- Stefano Lorenzi
- L. Luzzi
- Antonio Cammi
- Gianluigi Rozza
- Andrea Manzoni
- Alfio Maria Quarteroni
- Anthony T. Patera
- Paolo Pacciarini
In Statistics and Probability
Citations in the corpus
- POD-Galerkin method for finite volume approximation of Navier–Stokes and RANS equations (2016, methodology): “The reduced order modelling is aimed at approximating a Partial Differential Equation (PDE) solution (or a set of Ordinary Differential Equations (ODEs)) with a reduced number of degrees of freedom [7].”