Session I.5 - Geometric Integration and Computational Mechanics
Monday, June 12, 15:00 ~ 15:30
Learning of symmetric models for variational dynamical systems from data
Christian Offen
Paderborn University, Germany - This email address is being protected from spambots. You need JavaScript enabled to view it.
Equations of motions of variational dynamical systems can be derived from an action functional defined by a Lagrangian. When the Lagrangian is not known, it can be identified from dynamical data using machine learning techniques. However, Lagrangians are not uniquely determined by the dynamics. In this talk, I will show a framework to learn symmetric models of Lagrangians. The system’s symmetries and conservation laws do not need to be known a priori but are identified automatically based on a Lie group framework. Learning symmetric over non-symmetric Lagrangians improves qualitative aspects of the model, helps the numerical integration of the data-driven model, and informs the user about important geometric properties of the system.
Joint work with Eva Dierkes (University of Bremen, Germany), Kathrin Flaßkamp (Saarland University, Germany), Yana Lishkova (University of Oxford, UK), Steffen Ridderbusch (University of Oxford, UK), Sina Ober-Blöbaum (Paderborn University, Germany) and Paul Scherer (Cambridge, UK).