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651-4096-02L 3 Credits BSC , MSC D-ERDW , D-MATH , D-PHYS
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Inverse Theory II: Applications

Prerequisites: The successful completion of 651-4096-00L Inverse Theory I: Basics is mandatory.
VVZ CR n/a

Last Updated: 2026-06-01 11:32:52

Abstract

This second part of the course on Inverse Theory provides an introduction to the numerical solution of large-scale inverse problems. Specific examples are drawn from different areas of geophysics and image processing. Students solve various model problems using Jupyter notebooks, and familiarize themselves with relevant open-source libraries.

Objective

This course provides numerical tools and recipes to solve (non)-linear inverse problems arising in nearly all fields of science and engineering. After successful completion of the class, the students will have a thorough understanding of suitable solution algorithms, common challenges and possible mitigations to infer parameters that govern large-scale physical systems from sparse data measurements. Prerequisites for this course are (i) 651-4096-00L Inverse Theory: Basics, (ii) basic programming skills.

Content

The class discusses several important concepts to solve (non)-linear inverse problems and demonstrates how to apply them to real-world data applications. All sessions are split into a lecture part in the first half, followed by tutorials using Jupyter notebooks in the second. The range of covered topics include: 1. Regularization filters and image deblurring 2. Travel-time tomography 3. Line-search methods 4. Adjoint methods and time reversal 5. Full-waveform inversion 6. Uncertainty quantification and (Hamiltonian) Monte Carlo method

Resources

Lecture Notes

Presentation slides and some background material will be provided.

General Information

Language
English
Levels
BSC , MSC
Frequency
Yearly recurring

Examination

Type
graded semester performance
Reports on 5 out of 6 projects

Course Components

Type Title Time & Place Hours
lecture with exercise Inverse Theory II: Applications
  • Wed 08:15-12:00 (NO F 11)
28 h semesterly

Offered In