Seminar Uncertainty Quantification & Inverse Problems

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This seminar will be taught in English.

The first meeting will be on Wednesday 20 April at 12:15-14:00.

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Description

Uncertainty quantification (UQ) is an interesting, fast growing research area aimed at developing methods to address the impact of parameter, data and model uncertainty in complex systems. In this seminar, we will mainly focus on the identification of parameters through observations of the response of the system - the inverse problem. The uncertainty in the solution of the inverse problem will be described via the Bayesian approach. We will derive Bayes' theorem in the setting of finite and infinite dimensional parameter spaces, and discuss properties such as well-posedness, statistical estimates and connections to classical regularization methods. In particular we will focus on methods and algorithms for the efficient approximation of the solution of the Bayesian inverse problem. Machine learning techniques will be considered to enhance the performance of the methods.

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