The Crank-Nicolson Mixed Finite Element Scheme and Its Reduced-Order Extrapolation Model for the Fourth-Order Nonlinear Diffusion Equations with Temporal Fractional Derivative

This paper presents a Crank–Nicolson mixed finite element method along with its reduced-order extrapolation model for a fourth-order nonlinear diffusion equation with Caputo temporal fractional derivative. By introducing the auxiliary variable v=−ε2Δu+f(u), the equation is reformulated as a second-order coupled system. A Crank–Nicolson mixed finite element scheme is established, and its stability is proven using a discrete fractional Gronwall inequality. Error estimates for the variables u and v are derived. Furthermore, a reduced-order extrapolation model is constructed by applying proper orthogonal decomposition to the coefficient vectors of the first several finite element solutions. This scheme is also proven to be stable, and its error estimates are provided. Theoretical analysis shows that the reduced-order extrapolation Crank–Nicolson mixed finite approach reduces the degrees of freedom from tens of thousands to just a few, significantly cutting computational time and storage requirements. Numerical experiments demonstrate that both schemes achieve spatial second-order convergence accuracy. Under identical conditions, the CPU time required by the reduced-order extrapolation Crank–Nicolson mixed finite model is only 1/60 of that required by the Crank–Nicolson mixed finite scheme. These results validate the theoretical analysis and highlight the effectiveness of the methods.

Authors

Institutions

Publication Details

Journal
Fractal and Fractional
Published
2025-12-01
DOI
https://doi.org/10.3390/fractalfract9120789
Citations
1
Primary Topic
Fractional Differential Equations Solutions
Type
article
Field-Weighted Citation Impact
1.37
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Crank-Nicolson Mixed Finite Element Scheme and Its Reduced-Order Extrapolation Model for the Fourth-Order Nonlinear Diffusion Equations with Temporal Fractional Derivative

Jiahua Wang, Hong Li, Xiaohui Chang, Xuehui Ren
1 citations
Fractal and Fractional
Fractional Differential Equations Solutions
1.37
article

The Crank-Nicolson Mixed Finite Element Scheme and Its Reduced-Order Extrapolation Model for the Fourth-Order Nonlinear Diffusion Equations with Temporal Fractional Derivative

Jiahua Wang, Hong Li, Xiaohui Chang, Xuehui Ren
article en
1 citations

Abstract

This paper presents a Crank–Nicolson mixed finite element method along with its reduced-order extrapolation model for a fourth-order nonlinear diffusion equation with Caputo temporal fractional derivative. By introducing the auxiliary variable v=−ε2Δu+f(u), the equation is reformulated as a second-order coupled system. A Crank–Nicolson mixed finite element scheme is established, and its stability is proven using a discrete fractional Gronwall inequality. Error estimates for the variables u and v are derived. Furthermore, a reduced-order extrapolation model is constructed by applying proper orthogonal decomposition to the coefficient vectors of the first several finite element solutions. This scheme is also proven to be stable, and its error estimates are provided. Theoretical analysis shows that the reduced-order extrapolation Crank–Nicolson mixed finite approach reduces the degrees of freedom from tens of thousands to just a few, significantly cutting computational time and storage requirements. Numerical experiments demonstrate that both schemes achieve spatial second-order convergence accuracy. Under identical conditions, the CPU time required by the reduced-order extrapolation Crank–Nicolson mixed finite model is only 1/60 of that required by the Crank–Nicolson mixed finite scheme. These results validate the theoretical analysis and highlight the effectiveness of the methods.

Fractal and FractionalVol. 9(12)
Inner Mongolia University of Finance and Economics (CN), Inner Mongolia University (CN)
Openalex Percentile: Top 15%
Fractional Differential Equations Solutions
1.37
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.