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A form of the Laplace transform is reviewed as a paradigm for an entire class of fractional functional transforms. Various of its properties are discussed. Such transformations should be useful in application to differential/integral…

Data Analysis, Statistics and Probability · Physics 2018-04-30 R. A. Treumann , W. Baumjohann

Computational methods for fractional differential equations exhibit essential instability. Even a minor modification of the coefficients or other entry data may switch good results to the divergent. The goal of this paper is to suggest the…

Numerical Analysis · Mathematics 2021-12-20 P. B. Dubovski , J. A. Slepoi

The numerical analysis of time fractional evolution equations with the second-order elliptic operator including general time-space dependent variable coefficients is challenging, especially when the classical weak initial singularities are…

Numerical Analysis · Mathematics 2021-03-02 Pin Lyu , Seakweng Vong

A development of an inverse first-order divided difference operator for functions of several variables is presented. Two generalized derivative-free algorithms builded up from Ostrowski's method for solving systems of nonlinear equations…

Numerical Analysis · Mathematics 2011-10-12 Miquel Grau-Sánchez , Miquel Noguera , Sergio Amat

We consider the numerical integration of non-autonomous separable parabolic equations using high order splitting methods with complex coefficients (methods with real coefficients of order greater than two necessarily have negative…

Numerical Analysis · Mathematics 2014-05-20 Muaz Seydaoğlu , Sergio Blanes

We propose conformable Adomian decomposition method (CADM) for fractional partial differential equations (FPDEs). This method is a new Adomian decomposition method (ADM) based on conformable derivative operator (CDO) to solve FPDEs. At the…

General Mathematics · Mathematics 2017-04-11 Omer Acan , Dumitru Baleanu

In this note, two numerical methods of solving fractional differential equations (FDEs) are briefly described, namely predictor-corrector approach of Adams-Bashforth-Moulton type and multi-step generalized differential transform method…

Numerical Analysis · Mathematics 2018-06-11 Alireza Momenzadeh , Sima Sarv Ahrabi

A new Monte-Carlo method for solving linear parabolic partial differential equations is presented. Since, in this new scheme, the particles are followed backward in time, it provides great flexibility in choosing critical points in…

Numerical Analysis · Mathematics 2025-10-20 Johan Carlsson

In this note, we present a new numerical method for solving backward stochastic differential equations. Our method can be viewed as an analogue of the classical finite element method solving deterministic partial differential equations.

Probability · Mathematics 2011-06-07 Penghui Wang , Xu Zhang

In this paper invariant subspace method has been employed for solving linear and non-linear fractional partial differential equations involving Caputo derivative. A variety of illustrative examples are solved to demonstrate the…

Analysis of PDEs · Mathematics 2017-04-18 Sangita Choudhary , Varsha Daftardar-Gejji

Scientific and engineering problems often involve parametric partial differential equations (PDEs), such as uncertainty quantification, optimizations, and inverse problems. However, solving these PDEs repeatedly can be prohibitively…

Numerical Analysis · Mathematics 2023-07-04 Jun Sur Richard Park , Xueyu Zhu

A reduced-order model algorithm, called ALP, is proposed to solve nonlinear evolution partial differential equations. It is based on approximations of generalized Lax pairs. Contrary to other reduced-order methods, like Proper Orthogonal…

Numerical Analysis · Mathematics 2014-03-04 Jean-Frédéric Gerbeau , Damiano Lombardi

In this paper, an easy-to-implement and computationally effective numerical method based on the new orthogonal hybrid functions is developed to solve system of fractional order differential equations numerically. The new orthogonal hybrid…

Numerical Analysis · Mathematics 2018-01-23 Seshu Kumar Damarla , Madhusree Kundu

In this paper, we propose high-order numerical methods for the Riesz space fractional advection-dispersion equations (RSFADE) on a {f}inite domain. The RSFADE is obtained from the standard advection-dispersion equation by replacing the…

Numerical Analysis · Mathematics 2020-04-03 Libo Feng , Pinghui Zhuang , Fawang Liu , Ian Turner , Jing Li

In this work, we investigate an inverse problem of recovering multiple orders in a time-fractional diffusion model from the data observed at one single point on the boundary. We prove the unique recovery of the orders together with their…

Numerical Analysis · Mathematics 2021-11-17 Bangti Jin , Yavar Kian

In this paper, we develop a numerical scheme for the space-time fractional parabolic equation, i.e., an equation involving a fractional time derivative and a fractional spatial operator. Both the initial value problem and the…

Numerical Analysis · Mathematics 2017-08-18 Andrea Bonito , Wenyu Lei , Joseph E. Pasciak

We present a new numerical tool to solve partial differential equations involving Caputo derivatives of fractional variable order. Three Caputo-type fractional operators are considered, and for each one of them an approximation formula is…

Classical Analysis and ODEs · Mathematics 2015-12-08 Dina Tavares , Ricardo Almeida , Delfim F. M. Torres

The aim of this paper is to develop and analyze numerical schemes for approximately solving the backward problem of subdiffusion equation involving a fractional derivative in time with order $\alpha\in(0,1)$. After using quasi-boundary…

Numerical Analysis · Mathematics 2020-10-28 Zhengqi Zhang , Zhi Zhou

We describe a new method for finding analytic solutions to some initial-boundary problems for partial differential equations with constant coefficients. The method is based on expanding the denominator of the Laplace transformed Green's…

Mathematical Physics · Physics 2013-08-22 Vojin Jovanovic , Sergiy Koshkin

Advanced optimization algorithms such as Newton method and AdaGrad benefit from second order derivative or second order statistics to achieve better descent directions and faster convergence rates. At their heart, such algorithms need to…

Machine Learning · Computer Science 2022-08-31 Yao Lu , Mehrtash Harandi , Richard Hartley , Razvan Pascanu
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