In recent years there has been an increasing interest in optimization problems constrained by random Partial Differential Equations (PDEs), since the latter are effective mathematical tools to take into account intrinsic randomness or partial knowledge of the system under study.

The first part of the talk will provide a gentle introduction to the topic, suitable even for undergraduate students, underlying challenges, peculiarities, and modern research questions. In the second part, I will give an overview of recent contributions that span from optimization algorithms, multilevel/sparse approximations and inner solvers tailored for the extremely large KKT systems. If time permits, I will also discuss an ongoing work that combines optimization algorithms, originally developed in the machine learning community, with importance sampling for the optimization of the Conditional-Value-At-Risk.

Meeting number (access code): 2511 043 8966

Meeting password: scUG8fRAB65