Analytical Methods for Bayesian Inverse Problems Related to Partial Differential Equations

Antonio Capella (National Autonomous University of Mexico/Universität Heidelberg) #

22-25 April 2024: Analytical Methods for Bayesian Inverse Problems Related to Partial Differential Equations

This lecture presents some basic topics on probability, Bayesian statistics theory, analytical results on partial differential equations (PDEs) and inverse problems. The combination of these methods led to general approaches that can be used to derive efficient and robust approximations to solve inverse problems with uncertainty quantification. By the end of this course, students will gain insights into how these methods contribute to providing more accurate and reliable schemes to solve inverse problems with uncertainty quantification.

In the practice sessions, we present specific inverse problems, allowing the students to actively engage with the material and develop practical solutions through coding exercises.

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