Linear Partial Differential Equations: Analysis and Numerics

Columns of a matrix inverse in 1D and 2D.

Colors of A-1 where A is a discretized ∇2 with Dirichlet boundary conditions. Top: several columns in 1d (Ω = [0,L] = [0,1]). Bottom: two columns in 2d (Ω = [1,-1] x [-1,1]. In both 1d and 2d, the location of minimum corresponds to the index of the column: this is the effect of the unit-vector "source" or "force" = 1 at that position (and = 0 elsewhere). (Image by Steven G. Johnson.)

Instructor(s)

MIT Course Number

18.303

As Taught In

Fall 2014

Level

Undergraduate

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Course Description

Course Features

Course Description

This course provides students with the basic analytical and computational tools of linear partial differential equations (PDEs) for practical applications in science engineering, including heat / diffusion, wave, and Poisson equations. Analytics emphasize the viewpoint of linear algebra and the analogy with finite matrix problems. Numerics focus on finite-difference and finite-element techniques to reduce PDEs to matrix problems. The Julia Language (a free, open-source environment) is introduced and used in homework for simple examples.

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Related Content

Steven Johnson. 18.303 Linear Partial Differential Equations: Analysis and Numerics. Fall 2014. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.


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