Talk1: Shape optimization and optimal control problems involving PDEs have been largely studied during last decades. In this talk, I will introduce several cases of such problems involving fluid equations. Two fundamental approach will be introduced: One is the traditional approach related to the constrained optimization methods, and the other one is a relatively new approach using the reinforcement learning.

Talk2: In this talk, I will introduce basic convergence results and proofs of fundamental optimization algorithms such as the gradient descent, the stochastic gradient descent, and some algorithms for constrained optimization problems.