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In numerical mathematics, the '''gradient discretisation method (GDM)''' is a framework which contains classical and recent numerical schemes for diffusion problems of various kinds: linear or non-linear, steady-state or time-dependent. The schemes may be conforming or non-conforming, and may rely on very general polygonal or polyhedral meshes (or may even be meshless).
Some core properties are required to prove the convergence of a GDM. These core properties enable complete proofs of convergence of the GDM for elliptic and parabolic problems, linear or non-linear. For linear problems, stationary or transient, error estimates can be established based on three indicators specific to the GDM <ref>'''R. Eymard, C. Guichard, and R. Herbin.''' Small-stencil 3d schemes for diffusive flows in porous media. M2AN, 46:265–290, 2012.</ref> (the quantities <math>C_{D}</math>, <math>S_{D}</math> and <math>W_{D}</math>, [[#The example of a linear diffusion problem|see below]]). For non-linear problems, the proofs are based on compactness techniques and do not require any non-physical strong regularity assumption on the solution or the model data
Any scheme entering the GDM framework is then known to converge on all these problems. This applies in particular to [[#Galerkin methods and conforming finite element methods|conforming Finite Elements]], [[#Mixed finite element|Mixed Finite Elements]], [[#Nonconforming finite element|nonconforming Finite Elements]], and, in the case of more recent schemes, the [[#Discontinuous Galerkin method|Discontinuous Galerkin method]], [[#Mimetic finite difference method and nodal mimetic finite difference method|Hybrid Mixed Mimetic method, the Nodal Mimetic Finite Difference method]], some Discrete Duality Finite Volume schemes, and some Multi-Point Flux Approximation schemes
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