Inverse Problems

News

Dates

Lectures Mon 10:15-12:00 T9/046 Dr. Vesa Kaarnioja
Exercises Mon 12:15-14:00 T9/046 Dr. Vesa Kaarnioja
Oral exam Mon August 1, 2022 A6/212  
Make-up oral exam Tue September 27, 2022 A6/212

Please contact vesa.kaarnioja@fu-berlin.de in advance to organize a personal exam appointment.

General information

Description

Mathematical measurement models describe the causal effects of physical systems based on their material properties, initial conditions or other model parameters. In many practical problems, we have measurement data of the outcomes of these so-called "forward models" and we wish to infer the model parameters which caused the observations. This is an inverse problem.

Inverse problems are intrinsically ill-posed: the reconstruction of the unknown quantity may be highly sensitive to noise in the measurements, or a unique solution may not exist. For these reasons, regularization is an essential tool in order to find solutions to inverse problems. In this course, we will consider both deterministic regularization methods and statistical Bayesian inference. We will discuss the main challenges related to inverse problems as well as the main solution techniques.

Target audience

The course is intended for mathematics students at the Master's level.

Prerequisites

Multivariable calculus, linear algebra, basic probability theory, and MATLAB (or some other programming language).

Completing the course

Passing the course exam and completing weekly exercises.

Registration

Lecture notes

Exercise sheets

Please note that the bonus exercises will not be graded and do not need to be returned.

Contact

Dr. Vesa Kaarnioja vesa.kaarnioja@fu-berlin.de Arnimallee 6, Room 212
Consulting hours: By appointment