Learning Hamiltonian Systems with Mono-Implicit Runge-Kutta Methods

Explore the intricacies of learning Hamiltonian systems through Mono Implicit Runge-Kutta methods in this 40-minute conference talk presented at GSI. Delve into the mathematical foundations and practical applications of these advanced numerical integration techniques, gaining insights into their role in simulating and analyzing complex dynamical systems. Discover how these methods can be applied to various fields, including physics, engineering, and computational science, to accurately model and predict the behavior of Hamiltonian systems. Enhance your understanding of numerical analysis and its intersection with machine learning in the context of studying conservative dynamical systems.

Learning Hamiltonian Systems with Mono Implicit Runge–Kutta Methods