Simo Särkkä

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements

Kundan Kumar, Shreya Das, Simo Särkkä

This paper proposes a Bayesian filtering-based approach for learning the dynamics of a physical system from partial, noisy measurements. We model the system dynamics using a Lagrangian mechanics formulation. As in Lagrangian neural networks (LNNs), we parameterize the kinetic and…Read more