A Bayesian filtering approach learns Lagrangian dynamics parameterized by neural networks from noisy measurements by forming a stochastic state-space model and jointly estimating parameters and states via maximum likelihood.
Rigatos,Modelling and Control for Intelligent Industrial Systems: Adaptive Algorithms in Robotics and Industrial Engineering
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A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements
A Bayesian filtering approach learns Lagrangian dynamics parameterized by neural networks from noisy measurements by forming a stochastic state-space model and jointly estimating parameters and states via maximum likelihood.