Separation of variables... Fourier transforms... Green's functions... The Eastern cut-off... There are many techniques in the undergraduate canon for solving linear partial differential equations (PDEs) that have been popularized in excellent introductory texts. I confess that “The Eastern Cut-off” is not a mathematical technique, but rather, it was a well-regarded method for the high jump. By coincidence, the early 1800s saw both the first publication of Joseph Fourier's investigations into solutions to the heat equation using trigonometric series and the introduction of the high jump as an athletic event. For more than 150 years, the Eastern cut-off and a collection of other approaches were “known” to be the best high jump techniques until Richard Fosbury shocked the track and field community by turning away from the bar and leaping over it backwards with better results than other athletes could achieve. The lesson is clear: Success can discourage innovation by leading one to believe that the current workable solution is the best and only solution. This section of SIAM Review addresses this issue by injecting new ideas into traditional courses, and “The Method of Fokas for Solving Linear Partial Differential Equations,” by Bernard Deconinck, Thomas Trogdon, and Vishal Vasan, is ideally suited to this purpose. Fokas's method is a relatively new approach that should supplement the existing basket of methods and techniques, including separation of variables, Fourier transform, Laplace transforms, and so forth. In the context of this tutorial, a solution is an explicit expression that solves the PDE and satisfies the given boundary and initial conditions. The explicit expression typically takes the form of integrals or summations involving these boundary and initial conditions. (An explicit expression composed of information that is not provided in the statement of the problem, such as Dirichlet in place of Neumann boundary data, would not be considered a solution.) The new approach is to transform the linear PDE into a local relation of the form \[ \frac\partial \rho\partial t + \frac\partial j\partial x = 0, \rho(x,t,k) = e^-ikx + ømega(k)t q(x,t), \] where $q(x,t)$ is the dependent variable in the PDE, $\omega(k)$ is the dispersion relation, and $j(x,t,k)$ is deduced from the PDE. To solve the PDE, one applies Green's Theorem to the local relation over the $(x,t)$ domain with boundaries that fit the initial and boundary data. The majority of the effort involves mapping the resulting line integrals into the available information along the boundaries of the domain. In a broader sense, more traditional integral transform techniques are suited to certain types of boundary data, while the method of Fokas is a route to an appropriate integral transform by mathematical inspection and manipulation. As such, it can be a more general approach to a broad category of problems. There are advantages and disadvantages to covering this approach in the undergraduate curriculum. The method of Fokas requires students to have a solid background in complex analysis in order to know which manipulations and symmetries will map the existing integral expressions to the initial and boundary information posed in the problem. The traditional methods in a first course in PDEs require little background beyond a course in ODEs, but at the same time these methods are limited to a small set of problems. For an undergraduate PDEs course in which students have had some complex analysis beforehand, or for a graduate course, the method of Fokas is an outstanding supplement that provides a fresh perspective on integral transforms and where one transform might be more useful than another. This particular tutorial is limited to constant coefficient evolution equations with one spatial dimension, but there are many extensions and potential student projects available by digging into the literature. Similar to the “Fosbury Flop,” the method of Fokas approaches familiar problems from a new direction, providing students and instructors with new insights into linear PDEs.
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