Niveau d'étude
Master 2
ECTS
3 crédits
Volume horaire
26h
Période de l'année
Semestre 3
Description
The lectures include:
(1) Notations and reminders: vectors, matrices, scalar products, norms, variances, covariances, linear systems.
(2) Over-determined linear problems: definition, examples, least squares, data error handling, weighting, model errors and statistics, norms.
(3) Under-determined and mixed-determined linear problems: Definition, examples, Lagrange multipliers, null space, minimum norm, regularisation, data error handling, weighting, model errors, Bayesian approaches.
(4) Non-linear problems: Definition, examples, linearisation, gradient methods, regularisation, model errors and statistics.
The lectures are supported by four practicals of four hours each.
Objectifs
This course is an introduction to inverse problems, so that students can use simple data management and interpretation techniques. The aim is for them to know and to understand the underlying assumptions of the techniques used, to understand the importance of good management of measurement errors, and to be able to assess the impact of these errors on the solutions obtained.
Heures d'enseignement
- Inverse problemsCours Magistral10h
- Inverse problemsTravaux Pratiques16h
Pré-requis nécessaires
Basic knowledge of mathematics
Dernière mise à jour le 3 juin 2025