Numerical approximation for ODEs and PDEs

Lecturer(s): Philippe MICHEL, Elisabeth MIRONESCU
Course ⋅ 18 hTC ⋅ 18 hAutonomy ⋅ 12 h


The training action aims to give students wishing to continue their engineering studies towards courses requiring an advanced level in mathematics the possibility of deepening the basic notions seen in S5 in the UE MTH with powerful theoretical tools. and to learn the theoretical bases which will be applied in the elective course (S8 - S9 - Mathematics and Risk Engineering option).

Palabras clave

Measure and integration theory, probability theory, topology, functional analysis, partial differential equations.


  1. Measure theory, integration, probability theory.
  2. Topology, functional analysis.

Learning Outcomes

  • Understand and demonstrate the theoretical elements of analysis and probability.
  • Give examples and counterexamples.
  • Mathematical modelling, notion of well-posed problems.


Final mark = 75% Knowledge + 25% Know-how Knowledge mark = 100% final exam + 0% continuous assessment Know-how mark = 0% final exam + 100% continuous assessment