S
Steven Diot
Researcher at Los Alamos National Laboratory
Publications - 15
Citations - 1059
Steven Diot is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Finite volume method & Conservation law. The author has an hindex of 9, co-authored 15 publications receiving 848 citations. Previous affiliations of Steven Diot include University of Toulouse & Paul Sabatier University.
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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.
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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.
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Improved detection criteria for the multi-dimensional optimal order detection (MOOD) on unstructured meshes with very high-order polynomials
TL;DR: Numerical results on advection problems and hydrodynamics Euler equations are presented to show that the MOOD method is effectively high-order, intrinsically positivity-preserving on hydrodynamic test cases and computationally efficient.
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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.
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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.