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Numerical methods for fractional calculus attract increasing interests due to its wide applications in various fields such as physics, mechanics, etc. In this paper, we focus on constructing high-order algorithms for Riesz derivatives,…

Numerical Analysis · Mathematics 2016-11-22 Hengfei Ding , Changpin Li

Fractional diffusion equations are widely used to describe anomalous diffusion processes where the characteristic displacement scales as a power of time. For processes lacking such scaling the corresponding description may be given by…

Statistical Mechanics · Physics 2007-05-23 I. M. Sokolov , A. V. Chechkin , J. Klafter

We consider the discretization of time-space diffusion equations with fractional derivatives in space and either 1D or 2D spatial domains. The use of implicit Euler scheme in time and finite differences or finite elements in space, leads to…

Numerical Analysis · Mathematics 2019-08-13 Stefano Massei , Mariarosa Mazza , Leonardo Robol

A variety of simulation methodologies have been used for modeling reaction-diffusion dynamics -- including approaches based on Differential Equations (DE), the Stochastic Simulation Algorithm (SSA), Brownian Dynamics (BD), Green's Function…

Chemical Physics · Physics 2021-05-21 Marcus Thomas , Russell Schwartz

A three-level explicit time-split MacCormack scheme is proposed for solving the two-dimensional nonlinear reaction-diffusion equations. The computational cost is reduced thank to the splitting and the explicit MacCormack scheme. Under the…

Numerical Analysis · Mathematics 2020-12-02 Eric Ngondiep

A Spectral Difference (SD) algorithm on tensor-product elements which solves the reacting compressible Navier-Stokes equations (NSE) is presented. The classical SD algorithm is shown to be unstable when a multispecies gas where…

Computational Physics · Physics 2021-12-20 Thomas Marchal , Hugues Deniau , Jean-François Boussuge , Bénédicte Cuenot , Renaud Mercier

Diffusion models have emerged as powerful generative tools with applications in computer vision and scientific machine learning (SciML), where they have been used to solve large-scale probabilistic inverse problems. Traditionally, these…

Context: The technique of disentangling has been applied to numerous high-precision studies of spectroscopic binaries and multiple stars. Although, its possibilities have not yet been fully understood and exploited. Aims: Theoretical…

Instrumentation and Methods for Astrophysics · Physics 2009-11-13 Petr Hadrava

We introduce a \textit{non-modal} analysis technique that characterizes the diffusion properties of spectral element methods for linear convection-diffusion systems. While strictly speaking only valid for linear problems, the analysis is…

Computational Physics · Physics 2019-01-30 Pablo Fernandez , Rodrigo Moura , Gianmarco Mengaldo , Jaime Peraire

Stellar convection poses two main gargantuan challenges for astrophysical fluid solvers: low-Mach number flows and minuscule perturbations over steeply stratified hydrostatic equilibria. Most methods exhibit excessive numerical diffusion…

Fluid Dynamics · Physics 2026-04-09 D. A. Velasco-Romero , R. Teyssier

Spectral methods provide highly accurate numerical solutions for partial differential equations, exhibiting exponential convergence with the number of spectral nodes. Traditionally, in addressing time-dependent nonlinear problems, attention…

Numerical Analysis · Mathematics 2024-06-05 Dibyendu Adak , M. Engin Danis , Duc P. Truong , Kim Ø. Rasmussen , Boian S. Alexandrov

We develop a sparse spectral method for a class of fractional differential equations, posed on $\mathbb{R}$, in one dimension. These equations can include sqrt-Laplacian, Hilbert, derivative and identity terms. The numerical method utilizes…

Numerical Analysis · Mathematics 2024-06-12 Ioannis P. A. Papadopoulos , Sheehan Olver

The reaction-diffusion equation is one of the cornerstones equations in applied science and engineering. In the present study, a deep neural network has been trained in order to predict the solution of the equation with different…

Machine Learning · Computer Science 2019-12-12 Amin Karimi Monsefi , Rana Bakhtiyarzade

In this paper we investigate a sub-diffusion equation for simulating the anomalous diffusion phenomenon in real physical environment. Based on an equivalent transformation of the original sub-diffusion equation followed by the use of a…

Numerical Analysis · Mathematics 2018-03-29 Zongze Yang , Jungang Wang , Yan Li , Yufeng Nie

We describe an algorithm for the numerical solution of second order linear differential equations in the highly-oscillatory regime. It is founded on the recent observation that the solutions of equations of this type can be accurately…

Numerical Analysis · Mathematics 2015-06-23 James Bremer

In this work, we explore a time-fractional diffusion equation of order $\alpha \in (0,1)$ with a stochastic diffusivity parameter. We focus on efficient estimation of the expected values (considered as an infinite dimensional integral on…

Numerical Analysis · Mathematics 2024-09-04 Josef Dick , Hecong Gao , William McLean , Kassem Mustapha

This paper deals with the solution of unified fractional reaction-diffusion systems. The results are obtained in compact and elegant forms in terms of Mittag-Leffler functions and generalized Mittag-Leffler functions, which are suitable for…

Classical Analysis and ODEs · Mathematics 2014-09-11 R. K. Saxena , A. M. Mathai , H. J. Haubold

Fractional differential equations (FDEs) are an extension of the theory of fractional calculus. However, due to the difficulty in finding analytical solutions, there have not been extensive applications of FDEs until recent decades. With…

Numerical Analysis · Mathematics 2020-07-20 Nirupama Bhattacharya , Gabriel A. Silva

This study concerns the efficiency of time-spectral methods for numerical solution of differential equations. It is found that the time-spectral method GWRM demonstrates insensitivity to stiffness and chaoticity due to the implicit nature…

Numerical Analysis · Mathematics 2024-06-05 Jan Scheffel

The variable two-step backward differentiation formula (BDF2) is revisited via a new theoretical framework using the positive semi-definiteness of BDF2 convolution kernels and a class of orthogonal convolution kernels. We prove that, if the…

Numerical Analysis · Mathematics 2022-01-05 Hong-lin Liao , Zhimin Zhang
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