Local Error Estimate of an L1-Finite Difference Scheme for the Multiterm Two-Dimensional Time-Fractional Reaction–Diffusion Equation with Robin Boundary Conditions

Hou, Jian; Meng, Xiangyun; Wang, J. R.; Han, Yanfeng; Yu, Yongguang

doi:10.3390/fractalfract7060453

Fractal Fract

2023

DOI: 10.3390/fractalfract7060453

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Local Error Estimate of an L1-Finite Difference Scheme for the Multiterm Two-Dimensional Time-Fractional Reaction–Diffusion Equation with Robin Boundary Conditions

Jian Hou

Xiangyun Meng

J. R. Wang

et al.

Abstract: In this paper, the numerical method for a multiterm time-fractional reaction–diffusion equation with classical Robin boundary conditions is considered. The full discrete scheme is constructed with the L1-finite difference method, which entails using the L1 scheme on graded meshes for the temporal discretisation of each Caputo fractional derivative and using the finite difference method on uniform meshes for spatial discretisation. By dealing with the discretisation of Robin boundary conditions carefully, sharp… Show more

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2024

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Cited by 2 publications

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Solution of Inverse Problem for Diffusion Equation with Fractional Derivatives Using Metaheuristic Optimization Algorithm

Brociek,

Goik,

Miarka

et al. 2024

Informatica

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The article focuses on the presentation and comparison of selected heuristic algorithms for solving the inverse problem for the anomalous diffusion model. Considered mathematical model consists of time-space fractional diffusion equation with initial boundary conditions. Those kind of models are used in modelling the phenomena of heat flow in porous materials. In the model, Caputo’s and Riemann-Liouville’s fractional derivatives were used. The inverse problem was based on identifying orders of the derivatives and recreating fractional boundary condition. Taking into consideration the fact that inverse problems of this kind are ill-conditioned, the problem should be considered as hard to solve. Therefore,to solve it, metaheuristic optimization algorithms popular in scientific literature were used and their performance were compared: Group Teaching Optimization Algorithm (GTOA), Equilibrium Optimizer (EO), Grey Wolf Optimizer (GWO), War Strategy Optimizer (WSO), Tuna Swarm Optimization (TSO), Ant Colony Optimization (ACO), Jellyfish Search (JS) and Artificial Bee Colony (ABC). This paper presents computational examples showing effectiveness of considered metaheuristic optimization algorithms in solving inverse problem for anomalous diffusion model.

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Solution of Inverse Problem for Diffusion Equation with Fractional Derivatives Using Metaheuristic Optimization Algorithm

Brociek,

Goik,

Miarka

et al. 2024

Informatica

View full text Add to dashboard Cite

show abstract

Numerical Solution for the Heat Conduction Model with a Fractional Derivative and Temperature-Dependent Parameters

Brociek,

Hetmaniok,

Słota

2024

Symmetry

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This paper presents the numerical solution of the heat conduction model with a fractional derivative of the Riemann–Liouville type with respect to the spatial variable. The considered mathematical model assumes the dependence on temperature of the material parameters (such as specific heat, density, and thermal conductivity) of the model. In the paper, the boundary conditions of the first and second types are considered. If the heat flux equal to zero is assumed on the left boundary, then the thermal symmetry is obtained, which results in a simplification of the problem and the possibility of considering only half the area. The numerical examples presented in the paper illustrate the effectiveness and convergence of the discussed computational method.

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Local Error Estimate of an L1-Finite Difference Scheme for the Multiterm Two-Dimensional Time-Fractional Reaction–Diffusion Equation with Robin Boundary Conditions

Cited by 2 publications

References 28 publications

Solution of Inverse Problem for Diffusion Equation with Fractional Derivatives Using Metaheuristic Optimization Algorithm

Solution of Inverse Problem for Diffusion Equation with Fractional Derivatives Using Metaheuristic Optimization Algorithm

Numerical Solution for the Heat Conduction Model with a Fractional Derivative and Temperature-Dependent Parameters

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