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An adaptive memory method for accurate and efficient computation of the Caputo fractional derivative

  • Daegeun Yoon and Donghyun You EMAIL logo
Published/Copyright: October 28, 2021

Abstract

A fractional derivative is a temporally nonlocal operation which is computationally intensive due to inclusion of the accumulated contribution of function values at past times. In order to lessen the computational load while maintaining the accuracy of the fractional derivative, a novel numerical method for the Caputo fractional derivative is proposed. The present adaptive memory method significantly reduces the requirement for computational memory for storing function values at past time points and also significantly improves the accuracy by calculating convolution weights to function values at past time points which can be non-uniformly distributed in time. The superior accuracy of the present method to the accuracy of the previously reported methods is identified by deriving numerical errors analytically. The sub-diffusion process of a time-fractional diffusion equation is simulated to demonstrate the accuracy as well as the computational efficiency of the present method.

Appendix A. B(m, α) ⩽ c(α)mα

For 0 < α < 1, the function B(m, α) in Eq. (4.23) is expressed as follows:

B(m,α)=k=0m1(2mk)1α{2(2mk1)+α}(2mk1)1α{2(2mk)α}. (A.1)

By letting p = 2mk, Eq. (A.1) is rewritten as follows:

B(m,α)=p=m+12mp1α{2(p1)+α}(p1)1α(2pα). (A.2)

Using the Taylor series expansion, (p − 1)1−α is expanded as follows:

(p1)1α=p1α(1α)pα12(1α)αpα116(1α)α(α+1)pα2124(1α)α(α+1)(α+2)pα3O(pα4). (A.3)

By substituting Eq. (A.3) into Eq. (A.2), Eq. (A.2) is recast in terms of p as follows:

B(m,α)=16α(1α)(2α)p=m+12mpα1+O(pα2). (A.4)

For the summation in Eq. (A.4) we have the following inequality:

p=m+12m(2m)α1<p=m+12mpα1<p=m+12mmα1,(2α1)mα<p=m+12mpα1<mα. (A.5)

Therefore, there exists a real constant c(α) such that B(m, α) ⩽ c(α)mα.

Acknowledgements

This research was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2015R1A2A1A15056086 and NRF-2019K1A3A1A74107685).

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Received: 2020-03-01
Revised: 2021-07-25
Published Online: 2021-10-28
Published in Print: 2021-10-26

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