Overview

This monograph presents  the Gradient Discretisation Method (GDM), which is a unified convergence analysis framework for numerical methods for elliptic and parabolic partial differential equations. The results obtained by the GDM cover both stationary and transient models; error estimates are provided for linear (and some non-linear) equations, and convergence is established for a wide range of fully non-linear models (e.g. Leray–Lions equations and degenerate parabolic equations such as the Stefan or Richards models). The GDM applies to a diverse range of methods, both classical (conforming, non-conforming, mixed finite elements, discontinuous Galerkin) and modern (mimetic finite differences, hybrid and mixed finite volume, MPFA-O finite volume), some of which can be built on very general meshes.

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9783319790411

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Jérôme Droniou is Associate Professor at Monash University, Australia. His research focuses on elliptic and parabolic PDEs. He has published many papers on theoretical and numerical analysis of models with singularities or degeneracies, including convergence analysis of schemes without regularity assumptions on the data or solutions. Robert Eymard is professor of mathematics at Université Paris-Est Marne-la-Vallée. His research concerns  the design and analysis of numerical methods, mainly applied to fluid flows in porous media and incompressible Navier-Stokes equations. Thierry Gallouet is  professor at the University of Aix-Marseille. His research focuses on the analysis of partial differential equations and the approximation of their solutions by numerical schemes.  Cindy Guichard is assistant professor at Sorbonne Université. Her research is mainly focused on numerical methods for nonlinear fluid flows problems,  including  coupled  elliptic or parabolic equations  and  hyperbolic equations.  Raphaèle Herbin is professor at the University of Aix-Marseille. She is a specialist of numerical schemes for partial differential equations, with application to incompressible and compressible fluid flows.

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