Course description:
In many applications, we are interested in studying an object of interest x. However, its properties are often not directly measurable. Instead, we only observe y = F(x), where the function F is known. To infer about x from the observation y, we seek an (approximate) inverse mapping x ≈ F^-1(y). This process is known as solving the inverse problem.
In this course, we will cover three topics. The first is classical regularization theory, which highlights the difficulties of solving inverse problems related to noisy data. In the second part, we move to the variational formulation of inverse problems and discuss optimization strategies. Finally, the last part focuses on explainable data-driven and network-based approaches for inverse problems.
Lectures:
Monday: 16:15-17:45 MA 005
Thursday: 16:15-17:45 MA 043
- Trainer/in: Jonas Jens Erhard Walter Bresch
- Trainer/in: Oleh Melnyk