Fall 2015 - APMA 900 G100

Asymptotic Analysis of Differential Equations (4)

Class Number: 8835

Delivery Method: In Person


  • Course Times + Location:

    Sep 8 – Dec 7, 2015: Wed, Fri, 10:30 a.m.–12:20 p.m.



Analysis and computation of classical problems from applied mathematics such as eigenfunction expansions, integral transforms, and stability and bifurcation analyses. Methods include perturbation, boundary layer and multiple-scale analyses, averaging and homogenization, integral asymptotics and complex variable methods as applied to differential equations.


The course begins with an overview of classes of ordinary and partial differential equations (ODEs & PDEs) that are solved by exact methods. Fourier methods for solving linear DEs are extended to integral solution methods that include the Fourier and Laplace transforms. Investigation of this solution perspective establishes the close connection between complex variable theory and DEs. Other generalizations lead to the development of Sturm-Liouville eigenfunctions, function (Hilbert) spaces and special functions. But many ODEs and PDEs encountered in applications are not amenable to exact solution. Nonetheless, a variety of so-called asymptotic methods are available for extracting analytical understanding. These approximate methods can also be useful in benchmarking numerically-computed solutions, and even decoding exact solutions whose formula complexity defies interpretation. Perturbation theory analyzes problems that are “nearby” to those with known exact properties. This perspective also gives mathematical insight into the consequences of approximations which neglect complicating effects in the development of model equations. Yet other types of asymptotic methods address singular situations where small changes to DE problems have a large impact on the solution. Examples include techniques known as: multiple-scale, averaging, WKB (geometrical optics) and boundary-layer methods. Lectures will be based upon a case-study approach of ODE & PDE examples. Computational illustration will be an important tool for the lectures and assigned work. Computer visualization and numerical computing will involve the use and modification of Matlab scripts.



Undergraduate introduction to ODEs and linear PDEs.
Other useful background includes real & complex analysis, elementary numerical analysis and/or scientific computing. (SFU undergraduates with Math 418 credit are welcome to request enrollment.)



Introduction to perturbation methods [electronic resource] / Mark H. Holmes.

NOTE:  This is available for unlimited user access via the library website at http://troy.lib.sfu.ca/record=b6194008~S1a


Applied asymptotic analysis / Peter D. Miller

Foundations of applied mathematics / Michael D. Greenberg.

Graduate Studies Notes:

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Registrar Notes:

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