@inbook{e59ef082a19f417594212e41b0c964ca,
title = "Error Estimates for the Gradient Discretisation Method on Degenerate Parabolic Equations of Porous Medium Type",
abstract = "The gradient discretisation method (GDM) is a generic framework for the spatial discretisation of partial differential equations. The goal of this contribution is to establish an error estimate for a class of degenerate parabolic problems, obtained under very mild regularity assumptions on the exact solution. Our study covers well-known models like the porous medium equation and the fast diffusion equations, as well as the strongly degenerate Stefan problem. Several schemes are then compared in a last section devoted to numerical results.",
keywords = "Discontinuous Galerkin method, Error estimates, Fast diffusion, Gradient discretisation method, Hybrid mimetic mixed method, Numerical tests, Polytopal methods, Porous medium equation, Slow diffusion, Vertex approximate gradient method, Virtual element method",
author = "Cl{\'e}ment Canc{\`e}s and J{\'e}r{\^o}me Droniou and Cindy Guichard and Gianmarco Manzini and Olivares, {Manuela Bastidas} and Pop, {Iuliu Sorin}",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
doi = "10.1007/978-3-030-69363-3_2",
language = "English",
isbn = "9783030693626",
volume = "27",
series = "SEMA SIMAI Springer Series",
publisher = "Springer",
pages = "37--72",
editor = "{Antonio Di Pietro }, {Daniele } and Luca Formaggia and Roland Masson",
booktitle = "Polyhedral Methods in Geosciences",
address = "Switzerland",
}