Stable, fast, and adaptive splitting schemes for nonlinear doubly-degenerate diffusion equations

We consider linear iterative schemes for the time-discrete equations stemming from a class of nonlinear, doubly-degenerate parabolic equations. More precisely, the diffusion is nonlinear and may vanish or become multivalued for certain values of the unknown, so the parabolic equation becomes hyperbolic or elliptic, respectively. After performing an Euler implicit time-stepping, a splitting strategy is applied to the time-discrete equations. This leads to a formulation that is more suitable for dealing with the degeneracies. Based on this splitting, different iterative linearization strategies are considered, namely the Newton scheme, the L-scheme, and the modified L-scheme. We prove the convergence of the latter two schemes even for the double-degenerate case. In the nondegenerate case, we prove that the scheme is contractive, and the contraction rate is proportional to a nonnegative exponent of the time-step size. Moreover, following Stokke, Mitra, Storvik, Both, and Radu [Comput. Math. Appl. 152 (2023), pp. 155–167], an a posteriori estimator-based adaptive algorithm is developed to select the optimal parameters for the M-scheme, which accelerates its convergence. Numerical results are presented, showing that the M- and the M-adaptive schemes are more stable than the Newton scheme, as they converge irrespective of the mesh. Moreover, the adaptive M-scheme consistently out-competes not only the M/L-schemes, but also the Newton scheme showing quadratic convergence behavior.

Authors

Publication Details

Journal
Mathematics of Computation
Published
2026-08-26
DOI
https://doi.org/10.1090/mcom/4235
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Stable, fast, and adaptive splitting schemes for nonlinear doubly-degenerate diffusion equations

A. Javed, I. Pop, K. Mitra
Mathematics of Computation
Advanced Numerical Methods in Computational Mathematics
article

Stable, fast, and adaptive splitting schemes for nonlinear doubly-degenerate diffusion equations

A. Javed, I. Pop, K. Mitra
article en

Abstract

We consider linear iterative schemes for the time-discrete equations stemming from a class of nonlinear, doubly-degenerate parabolic equations. More precisely, the diffusion is nonlinear and may vanish or become multivalued for certain values of the unknown, so the parabolic equation becomes hyperbolic or elliptic, respectively. After performing an Euler implicit time-stepping, a splitting strategy is applied to the time-discrete equations. This leads to a formulation that is more suitable for dealing with the degeneracies. Based on this splitting, different iterative linearization strategies are considered, namely the Newton scheme, the L-scheme, and the modified L-scheme. We prove the convergence of the latter two schemes even for the double-degenerate case. In the nondegenerate case, we prove that the scheme is contractive, and the contraction rate is proportional to a nonnegative exponent of the time-step size. Moreover, following Stokke, Mitra, Storvik, Both, and Radu [Comput. Math. Appl. 152 (2023), pp. 155–167], an a posteriori estimator-based adaptive algorithm is developed to select the optimal parameters for the M-scheme, which accelerates its convergence. Numerical results are presented, showing that the M- and the M-adaptive schemes are more stable than the Newton scheme, as they converge irrespective of the mesh. Moreover, the adaptive M-scheme consistently out-competes not only the M/L-schemes, but also the Newton scheme showing quadratic convergence behavior.

Mathematics of Computation
Deutsche Forschungsgemeinschaft, Fonds Wetenschappelijk Onderzoek, Higher Education Commission, Pakistan
Openalex Percentile: Top 13%
Advanced Numerical Methods in Computational Mathematics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.