Title: Structural Inference of Dynamical Systems: Recent Development and Future Directions
Abstract: Dynamical systems are pervasive and critical in understanding phenomena across various domains, from the majestic dance of celestial bodies governed by gravity to the subtle ballet of chemical reactions. In the quest to unravel the complexities of dynamical systems, the initial imperative is to unveil their inherent structure, a key determinant of system organization. Achieving this necessitates the deployment of structural inference methods capable of deriving this structure from observed system behaviors. In this talk, I will give an overview on recent development of deep learning based methods for structural inference of dynamical systems, in particular methods based on variational auto-encoder (VAE). Through a comprehensive benchmarking study, I will present some key findings and iscuss future research directions.
Short Bio: Prof. Jun Pang received his PhD from Free University Amsterdam. He currently works in the Computer Science Department at the University of Luxembourg, and he is also a fellow of the Institute for Advanced Studies within the University of Luxembourg. He has long been devoted to research in formal methods, security and privacy, systems biology, and graph machine learning.
You can also join us via Zoom:
https://us06web.zoom.us/j/89201136639?pwd=4eqr4guHAfuTNEYCXEGi6cHnle3Qq8.1
ID meeting: 892 0113 6639
Password: 727039
