Discrete Gradient Methods Preserve the Energy Dissipation Law and Maximum Bound Principle of the Time-Fractional Allen–Cahn equation with General Nonlinear Potential

Abstract In this paper, we design two classes of numerical schemes for the time-fractional Allen–Cahn equation based on the discrete gradient method and the L1-type formula of the Riemann–Liouville fractional derivative. The suggested schemes are as accurate and efficient as a recent work [H. Liao, T. Tang and T. Zhou, An energy stable and maximum bound preserving scheme with variable time steps for time fractional Allen–Cahn equation, SIAM J. Sci. Comput. 43 2021, 5, A3503–A3526] for the time-fractional Allen–Cahn equation with a double-well potential function, but can address the time-fractional Allen–Cahn equation with a general potential function. Thanks to the orthogonal convolution technique on nonuniform time mesh: (i) the first type of schemes unconditionally preserves the discrete energy dissipation law; (ii) the second type of schemes is constructed by introducing the fixed-point iteration and stabilized factor method for the first scheme, and proven to be maximum bound principle preserving. Finally, extensive numerical comparisons are reported to verify the efficiency of the proposed schemes and the correctness of the theoretical results.

Authors

Institutions

Publication Details

Journal
Computational Methods in Applied Mathematics
Published
2026-05-27
DOI
https://doi.org/10.1515/cmam-2025-0182
Primary Topic
Fractional Differential Equations Solutions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Discrete Gradient Methods Preserve the Energy Dissipation Law and Maximum Bound Principle of the Time-Fractional Allen–Cahn equation with General Nonlinear Potential

Minghua Chen, Huiling Jiang, Dongdong Hu
Computational Methods in Applied Mathematics
Fractional Differential Equations Solutions
article

Discrete Gradient Methods Preserve the Energy Dissipation Law and Maximum Bound Principle of the Time-Fractional Allen–Cahn equation with General Nonlinear Potential

Minghua Chen, Huiling Jiang, Dongdong Hu
article en

Abstract

Abstract In this paper, we design two classes of numerical schemes for the time-fractional Allen–Cahn equation based on the discrete gradient method and the L1-type formula of the Riemann–Liouville fractional derivative. The suggested schemes are as accurate and efficient as a recent work [H. Liao, T. Tang and T. Zhou, An energy stable and maximum bound preserving scheme with variable time steps for time fractional Allen–Cahn equation, SIAM J. Sci. Comput. 43 2021, 5, A3503–A3526] for the time-fractional Allen–Cahn equation with a double-well potential function, but can address the time-fractional Allen–Cahn equation with a general potential function. Thanks to the orthogonal convolution technique on nonuniform time mesh: (i) the first type of schemes unconditionally preserves the discrete energy dissipation law; (ii) the second type of schemes is constructed by introducing the fixed-point iteration and stabilized factor method for the first scheme, and proven to be maximum bound principle preserving. Finally, extensive numerical comparisons are reported to verify the efficiency of the proposed schemes and the correctness of the theoretical results.

Computational Methods in Applied Mathematics
Hunan University of Science and Technology (CN), Jiangxi Science and Technology Normal University (CN), Xiangtan University (CN), Jiangxi Normal University (CN)
Affordable and clean energy
Openalex Percentile: Top 6%
Fractional Differential Equations Solutions
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.