Universität Wien

280390 VO MA PE 01 VO Inverse Problems (NPI) (2021W)

MIXED

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 10 participants
Language: English

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

The course will start from the 2nd week of semester, i.e. from October 14, due to organizational reasons.

Thursday 07.10. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 14.10. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 21.10. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 28.10. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 04.11. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 11.11. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 18.11. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 25.11. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 02.12. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 09.12. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 16.12. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 13.01. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 20.01. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday 27.01. 12:00 - 14:30 Seminarraum Paläontologie 2B311 3.OG UZA II

Information

Aims, contents and method of the course

The course deals with the resolution of ill-posed problems, e.g. inverse problems from physics and simple problems from data science of data fitting type. More precisely, the following aspects will be covered:
-- Linear regression (and statistical aspects of least squares)
-- Discretization of inverse problems
-- Ill-posed problems and rank deficiency
-- Tikhonov regularization
-- Nonlinear regression (Newton method etc).
-- Bayesian approach

All of these topics will be complemented with exercises (Matlab or Python).

Assessment and permitted materials

Oral examination

Minimum requirements and assessment criteria

Examination topics

Reading list

R.C. Aster, B. Borchers, C.H. Thurber: Parameter Estimation and Inverse Problems, Elsevier, 2013.

Association in the course directory

Last modified: Mo 26.09.2022 13:10