5th Symposium on Machine Learning and Dynamical Systems (MLDS 5)
& Differential Equations for Data Science (DEDS 2026)
Masukawa Hall, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, Japan
February 9-13, 2026
Masukawa Hall, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, Japan
February 9-13, 2026
The interface between the theory of dynamical systems and machine learning is a relatively unexplored but promising area. This symposium aims to explore this interface, uniting experts from both domains to advance our understanding in two key areas:
Machine Learning for Dynamical Systems: This approach emphasizes analyzing dynamical systems based on observed data, shifting from traditional analytical methods to a data-centric perspective while maintaining mathematical rigor.
Dynamical Systems for Machine Learning: Here, the objective is to apply the principles and tools of dynamical systems theory to examine and enhance machine learning algorithms.
This initiative marks a significant step in integrating the mathematical insights of the theory of dynamical systems with the practical capabilities of machine learning, fostering advancements in both disciplines.
This symposium is supported by the Japan Science and Technology Agency (JST) under CREST Grants Number JPMJCR2014, JPMJCR24Q1, and JPMJCR24Q6, by the Japan Society for the Promotion of Science (JSPS) under KAKENHI Grants Number 23K25780 and 24K21316, and by Research Institute for Mathematical Sciences (RIMS), Kyoto University.
Program
In preparation
General Information
In preparation
Organizers
Boumediene Hamzi (Caltech)
Yuka Hashimoto (NTT)
Isao Ishikawa (Kyoto University)
Hiroshi Kokubu (Kyoto University)
Hirofumi Notsu (Kanazawa University)