MATH-451 / 5 crédits

Enseignant: Buffa Annalisa

Langue: Anglais


Summary

The course pertains to the derivation, theoretical analysis and implementation of finite difference and finite element methods for the numerical approximation of partial differential equations in one or more dimensions.

Content

Keywords

Partial differential equations, finite difference method, finite element method, Galerkin approximation, stability and convergence analysis.

Learning Prerequisites

Required courses

Analysis I-II-III-IV, Numerical analysis.

Recommended courses

Functional analysis I, Measure and integration, Espaces de Sobolev et équations elliptiques, Advanced numerical analysis, Programming.

Important concepts to start the course

  • Basic knowledge of functional analysis: Banach and Hilbert spaces, L^p spaces.
  • Some knowledge on theory of PDEs: classical and weak solutions, existence and uniqueness.
  • Basic concepts in numerical analysis: stability, convergence, condition number, solution of linear systems, quadrature formulae, finite difference formulae, polynomial interpolation.

 

Learning Outcomes

By the end of the course, the student must be able to:

  • Identify features of a PDE relevant for the selection and performance of a numerical algorithm.
  • Assess / Evaluate numerical methods in light of the theoretical results.
  • Implement fundamental numerical methods for the solution of PDEs.
  • Choose an appropriate discretization scheme to solve a specific PDE.
  • Analyze numerical errors and stability properties.
  • Interpret results of a computation in the light of theory.
  • Prove theoretical properties of discretization schemes.
  • State theoretical properties of PDEs and corresponding discretization schemes.

Transversal skills

  • Use a work methodology appropriate to the task.
  • Write a scientific or technical report.
  • Use both general and domain specific IT resources and tools

Teaching methods

Ex cathedra lectures, exercises in the classroom and computer lab sessions.

Expected student activities

  • Attendance of lectures.
  • Completing exercises.
  • Solving simple problems on the computer.

Assessment methods

100% Written exam. The exam may involve the use of a computer.

A bonus of 0.5 points is given to students who deliver exercises when requested during the semester.

 

Supervision

Office hours Yes
Assistants Yes
Forum No

Prerequisite for

Numerical approximation of PDEs II, Numerical methods for conservation laws, Numerical methods for fluids, structures & electromagnetics

Dans les plans d'études

  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Numerical approximation of PDEs
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Numerical approximation of PDEs
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Numerical approximation of PDEs
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Numerical approximation of PDEs
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Numerical approximation of PDEs
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22