RMIT University
Browse

Recipes for efficient higher-order multiscale asymptotic analysis

conference contribution
posted on 2024-10-30, 19:17 authored by Hardik Vagh, Alireza Baghai - Wadji
Realistic models for physical phenomena in singular perturbation theory often involve multiscale processes which need to be regularized systematically and carefully. A case in point is when the roughness (non-smoothness) of the solution is confined to a small portion of the simulation domain. In the past decades considerable efforts have been made in developing asymptotic solutions for multiscale problems. However, the majority of works only provide the first few dominant expansion terms due to the complexity of the iteration involved in the so-called ldquoMethod of Matched Asymptotic Expansions.rdquo The primary objectives in this paper are (i) to establish a convenient symbolic notation for the derivation of the asymptotics to any order desired; (ii) to demonstrate that the involved matching process in the algorithms occurs on the boundary rather than on boundary layers; (iii) to propose a technique which revises the usage of the O - calculus by making redundant a variety of intermediate calculation steps in standard analyses. Details will be discussed in terms of a model problem.

History

Related Materials

  1. 1.
    ISBN - Is published in 9781424427383 (urn:isbn:9781424427383)

Start page

213

End page

218

Total pages

6

Outlet

Proceedings for 4th International Conference on Ultrawideband and Ultrashort Impulse Signals 2008,

Editors

Prof. Nikolay N Kolchigin

Name of conference

2008 4th International Conference on Ultrawideband and Ultrashort Impulse Signals, UWBUSIS

Publisher

IEEE

Place published

Piscataway, United States

Start date

2008-09-15

End date

2008-09-19

Language

English

Copyright

©2008 IEEE

Former Identifier

2006009940

Esploro creation date

2020-06-22

Fedora creation date

2009-10-18

Usage metrics

    Scholarly Works

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC