Raphaël Loubère
Institut de Mathématiques de Toulouse
63 Papers
137 Citations
Raphaël Loubère is an academic researcher from Institut de Mathématiques de Toulouse. The author has contributed to research in topics: Finite volume method & Euler equations. The author has an hindex of 24, co-authored 57 publications. Previous affiliations of Raphaël Loubère include University of Toulouse & Los Alamos National Laboratory.
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Papers
A posteriori subcell limiting of the discontinuous Galerkin finite element method for hyperbolic conservation laws
TL;DR: A novel a posteriori finite volume subcell limiter technique for the Discontinuous Galerkin finite element method for nonlinear systems of hyperbolic conservation laws in multiple space dimensions that works well for arbitrary high order of accuracy in space and time and that does not destroy the natural subcell resolution properties of the DG method.
363
A high-order finite volume method for systems of conservation laws-Multi-dimensional Optimal Order Detection (MOOD)
TL;DR: Numerical results on classical and demanding test cases for advection and Euler system are presented on quadrangular meshes to support the promising potential of the multi-dimensional Optimal Order Detection approach.
349
ReALE: A reconnection-based arbitrary-Lagrangian-Eulerian method
TL;DR: Performance of the new reconnection-based arbitrary-Lagrangian–Eulerian (ALE) method is demonstrated on series of numerical examples and it is shown to have superiority in comparison with standard ALE methods without reconnection.
215
The Multidimensional Optimal Order Detection method in the three-dimensional case: very high-order finite volume method for hyperbolic systems
TL;DR: In this article, the multidimensional optimal order detection (MOOD) method was extended to 3D mixed meshes composed of tetrahedra, hexahedral, pyramids, and prisms.
127
A New Family of High Order Unstructured MOOD and ADER Finite Volume Schemes for Multidimensional Systems of Hyperbolic Conservation Laws
TL;DR: The main finding of this paper is that the combination of ADER with MOOD generally outperforms the one of ADer and WENO either because at given accuracy MOOD is less expensive (memory and/or CPU time), or because it is more accurate for a given grid resolution.