Learning of Dynamic Processes

Date: Mon. June 5, 2023 at 11:00H

Organized by: FAU DCN-AvH, Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)

Title: Learning of Dynamic Processes
Speaker: Prof. Dr. Juan-Pablo Ortega
Affiliation: Nanyang Technological University (Singapore)

Abstract. The last decade has seen the emergence of learning techniques that use the computational power of dynamical systems for information processing. Some of those paradigms are based on architectures that are partially randomly generated and require a relatively cheap training effort, which makes them ideal in many applications. The need for a mathematical understanding of the working principles underlying this approach, collectively known as Reservoir Computing, has led to the construction of new techniques that put together well-known results in systems theory and dynamics with others coming from approximation and statistical learning theory. This combination has allowed in recent times to elevate Reservoir Computing to the realm of provable machine learning paradigms. As we will see in this talk, it also hints at various connections with kernel maps, structure-preserving algorithms, and physics-inspired learning.