Supremizer stabilization of POD–Galerkin approximation of parametrized steady incompressible Navier–Stokes equations
By Francesco Ballarin, Andrea Manzoni, Alfio Maria Quarteroni, Gianluigi Rozza. 2015
289 citations recorded by the source corpus.
Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6ecd0ce5ca5991db51e0fa6da07a8436b823a6a4
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
- Stefano Lorenzi
- Gianluigi Rozza
- Stefania Fresca
- Saddam Hijazi
- Andrea Mola
- Giovanni Stabile
- L. Luzzi
In Statistical and Nonlinear Physics
- Andrea Manzoni
- Stefano Lorenzi
- Gianluigi Rozza
- L. Luzzi
- Antonio Cammi
- Stefania Fresca
- Saddam Hijazi
- Andrea Mola
In Statistics and Probability
- Andrea Manzoni
- Stefano Lorenzi
- L. Luzzi
- Antonio Cammi
- Gianluigi Rozza
- Saddam Hijazi
- Andrea Mola
- Giovanni Stabile
Citations in the corpus
- POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (2021, background): “…minutes, required to achieve in the We remark that ensuring ROM stability in the classical POD-Galerkin framework usually requires additional computational efforts, such as a suitable enrichment of the velocity reduced basis, and a consequent increase of the size of the ROM; see, e.g., [6, 7, 58].”
- Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020, background): “d are prohibitive for decision-making tasks for many fast-query applications. In order to reduce costs, there has recently been signicant interest in the reduced-order modeling (ROM) of such systems [1, 2, 3, 4, 5, 6, 7]. As such, this eld nds extensive application in control [8, 9, 10, 11], multi-d”
- POD-Galerkin method for finite volume approximation of Navier–Stokes and RANS equations (2016, methodology): “For this purpose, it could be necessary employing stabilization techniques also for laminar flows [72].”
- POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder (2017, methodology): “As future developments the interest is into different efficient methodologies for the reconstruction of the pressure term and in particular to study the applicability of well known stabilization methods, used in the context of Galerkin ROM for finite elements [38,39], to a finite volume framework [4”