A New Mixed Fractional Derivative with Applications in Computational Biology

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2024-01-04 DOI:10.3390/computation12010007
K. Hattaf
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Abstract

This study develops a new definition of a fractional derivative that mixes the definitions of fractional derivatives with singular and non-singular kernels. This developed definition encompasses many types of fractional derivatives, such as the Riemann–Liouville and Caputo fractional derivatives for singular kernel types, as well as the Caputo–Fabrizio, the Atangana–Baleanu, and the generalized Hattaf fractional derivatives for non-singular kernel types. The associate fractional integral of the new mixed fractional derivative is rigorously introduced. Furthermore, a novel numerical scheme is developed to approximate the solutions of a class of fractional differential equations (FDEs) involving the mixed fractional derivative. Finally, an application in computational biology is presented.
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一种新的混合分数衍生物在计算生物学中的应用
本研究提出了分数导数的新定义,它混合了具有奇异和非奇异内核的分数导数的定义。这个新定义涵盖了多种类型的分数导数,如奇异内核类型的黎曼-刘维尔分数导数和卡普托分数导数,以及非奇异内核类型的卡普托-法布里齐奥分数导数、阿坦加纳-巴莱阿努分数导数和广义哈塔夫分数导数。新混合分数导数的关联分数积分被严格引入。此外,还开发了一种新的数值方案,用于近似求解涉及混合分数导数的一类分数微分方程(FDE)。最后,介绍了在计算生物学中的应用。
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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