Inverse Problems (Sommersemester 2023)
News
- (NEW) The deadline for the sixth exercise sheet (here) will be Tuesday June 13, 10:15.
Dates
| Lectures | Mon 10:15-12:00 | A6/025/026 | Dr. Vesa Kaarnioja |
| Exercises | Tue 10:15-12:00 | A6/007/008 | Dr. Vesa Kaarnioja |
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 and above.
Prerequisites
Multivariable calculus, linear algebra, basic probability theory, and MATLAB (or some other programming language).
Completing the course
The conditions for completing this course are successfully completing and submitting at least 60% of the course's exercises and successfully passing the course exam.
Registration
- Please register to the course via Campus Management (CM), then you will be automatically registered in MyCampus/Whiteboard as well. Please note the deadlines indicated there. For further information and in case of any problems, please consult the Campus Management's Help for Students.
- Non-FU students should register to the course in KVV (Whiteboard).
Lecture notes
Lecture notes will be published here after each week's lecture.
- Week 1: Introduction, preliminary functional analysis
- Week 2: Spectral theory of compact operators
- Week 3: Introduction to X-ray tomography (files: tomodemo.py / tomodemo.m)
- Week 4: Fredholm equation and its solvability, truncated SVD, and the Morozov discrepancy principle (files: tomo_tsvd.py, heateq_tsvd.py / tomo_tsvd.m, heateq_tsvd.m)
- Week 5: Tikhonov regularization (files: heateq_tikhonov.py / heateq_tikhonov.m)
- Week 6: Truncated iterative methods: Landweber–Fridman iteration and the conjugate gradient method (files: heateq_cg.py / heateq_cg.m)
- Week 7: Total variation regularization for X-ray tomography (files: sino.mat, tvdemo.py / tvdemo.m)
- Week 8: Brief overview of probability theory and Bayes' formula for inverse problems (files: source.py / source.m)
Exercise sheets
Weekly exercises will be published here after each lecture.
- Exercise 1 (model solutions)
- Exercise 2 (model solutions)
- Exercise 3 (model solutions)
- Exercise 4 (files: sino.mat; model solutions)
- Exercise 5 (files: sino.mat; model solutions)
- Exercise 6 (deadline: Tuesday 13 June, 2023, at 10:15)
Contact
| Dr. Vesa Kaarnioja | vesa.kaarnioja@fu-berlin.de | Arnimallee 6, room 212 Consulting hours: By appointment |