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Quarterly of Applied Mathematics

Quarterly of Applied Mathematics

Online ISSN 1552-4485; Print ISSN 0033-569X

   
 
 

 

The numerical solution of semidiscrete linear evolution problems on the finite interval using the Unified Transform Method


Authors: Jorge Cisneros and Bernard Deconinck
Journal: Quart. Appl. Math. 80 (2022), 739-786
MSC (2020): Primary 65M22, 65M06; Secondary 39A27, 39A14
DOI: https://doi.org/10.1090/qam/1626
Published electronically: June 29, 2022
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Abstract: We study a semidiscrete analogue of the Unified Transform Method introduced by A. S. Fokas, to solve initial-boundary-value problems for linear evolution partial differential equations with constant coefficients on the finite interval $x \in (0,L)$. The semidiscrete method is applied to various spatial discretizations of several first and second-order linear equations, producing the exact solution for the semidiscrete problem, given appropriate initial and boundary data. From these solutions, we derive alternative series representations that are better suited for numerical computations. In addition, we show how the Unified Transform Method treats derivative boundary conditions and ghost points introduced by the choice of discretization stencil and we propose the notion of “natural” discretizations. We consider the continuum limit of the semidiscrete solutions and compare with standard finite-difference schemes.


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Additional Information

Jorge Cisneros
Affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington 98195-2420
MR Author ID: 1242436
ORCID: 0000-0003-4493-0536
Email: jorgec5@uw.edu

Bernard Deconinck
Affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington 98195-2420
MR Author ID: 613566
Email: bernard@amath.washington.edu

Keywords: Continuum limit, finite difference, finite interval, ghost points, semidiscrete linear problem, Unified Transform Method
Received by editor(s): February 4, 2022
Received by editor(s) in revised form: April 24, 2022
Published electronically: June 29, 2022
Additional Notes: This work was supported by the Graduate Opportunities & Minority Achievement Program Fellowship from the University of Washington and the Ford Foundation Predoctoral Fellowship (the first author). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding sources.
Article copyright: © Copyright 2022 Brown University