Efficient Learning of Dynamical Systems

The project aims to develop methods that learn complex dynamical models through algorithms that actively explore the behavior of the system. The key challenge is to trade the cost of exploration with the future benefits from the resulting improved knowledge.

This area, sometimes named dual control theory, is now vitalized from recent progress in machine learning and statistical estimation. The project will both develop new theory and algorithms and work with applications.