Computational Inverse Problems and Uncertainty Quantification
Inverse problems arise in an many science and engineering applications. Examples range from exploration of the inside of our planet to understanding the dynamics of Antarctic ice sheets and to medical imaging problems. In all these applications, model parameters must be estimated from noisy and indirect observational data.
Uncertainty is integral to this endeavor: observational errors, model errors, and issues of ill-posedness yield uncertainties in model parameters.
I will give an overview over the fast developing field of inverse uncertainty quantification for problems governed by PDEs. While this field builds on ideas from inverse problem theory, Bayesian probability, and PDE-constrained optimization, it is not required that you have a strong background in all these fields to attend and hopefully benefit from this course. I am planning on rapidly summarizing the available background and to sometimes build on your
intuition, while pointing out the relevant research literature for further reading. I am also planning on incorporating hands-on sessions that will allow you to experiment with inverse problems and basic concepts of uncertainty quantification.
I will try to adjust the course to your background and interests and would like to have an idea about the number of interested students for the hands-on sessions. Thus, you are required to register for this course.
For registration please send an email to Angela Puchert (firstname.lastname@example.org
Date and Place