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Linear Partial Differential Equations: Analysis and Numerics, Fall 2010

(Complete Item Description)

Abstract:: 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.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Linear Partial Differential Equations, Fall 2004

(Complete Item Description)

Abstract:: The classical partial differential equations of applied mathematics: diffusion, Laplace/Poisson, and wave equations. Methods of solution, such as separation of variables, Fourier series and transforms, eigenvalue problems. Green's function methods are emphasized. 18.04 or 18.112 are useful, as well as previous acquaintance with the equations as they arise in scientific applications.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Linear Partial Differential Equations, Fall 2005

(Complete Item Description)

Abstract:: The classical partial differential equations of applied mathematics: diffusion, Laplace/Poisson, and wave equations. Methods of solution, such as separation of variables, Fourier series and transforms, eigenvalue problems. Green's function methods are emphasized. 18.04 or 18.112 are useful, as well as previous acquaintance with the equations as they arise in scientific applications.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Linear Partial Differential Equations, Fall 2006

(Complete Item Description)

Abstract:: The classical partial differential equations of applied mathematics: diffusion, Laplace/Poisson, and wave equations. Methods of solution, such as separation of variables, Fourier series and transforms, eigenvalue problems. Green's function methods are emphasized. 18.04 or 18.112 are useful, as well as previous acquaintance with the equations as they arise in scientific applications.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

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