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Numerical solution of PDEs

Course Type: 
PhD Course
Academic Year: 
March - May
40 h


Room 128: 8/3, 14/3,  21/3,  5/4, 12/4, 19/4

Room 139: 15/3, 22/3 

Room 133: 4/4, 11/4, 18/4, 25/4, 2/5, 9/5, 10/5, 16/5, 17/5

Room 134: 3/5


This course provides a high level overview on the numerical solution of Partial Differential Equations (PDEs). The course focuses on the Finite Element Methods (FEMs) but insights on all major classes of numerical methods will be discusses. Numerical methods will be presented and analysed within a rigorous mathematical setting. Key aspects such as consistency, stability, and convergence will be thoroughly investigated, providing the guidelines for the correct choice and implementation of numerical methods for a range of problems. The lectures will be completed by computer classes based on the Python language. These are hands-on sessions where codes are produced to test the properties of the numerical algorithms seen in the lectures, helping everyone to build their own suits of numerical codes. Synopsis 1. Elliptic problems. Finite Differences discretisation; Discrete Maximum principle, consistency, stability, and convergence.consistency, stability, and convergence. 2. Basic notions on functional spaces. Weak formulations; Lax-Milgram lemma. 3. The method of Galerkin; Lemma of Cea. 4. Finite Element Methods (FEM); analysis, implementation, conditioning 5. Generalised Galerkin method; the lemma of Strang. 6. Convection-reaction-diffusion problems; the streamline diffusion method. 7. Parabolic problems. Weak formulations; well-posedness, energy estimates. 8. Finite Differences discretisation; consistency, stability, and convergence. 9. FEM for parabolic problems; elliptic projection, convergence analysis. 10. Hyperbolic problems, Conservation laws 11. Finite Differences, Finite Volume, and Finite Element Methods

TBC(to be checked)
Lectures are also streamed on zoom. For all details, see github page:
Next Lectures: 

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