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Learning Lagrangian dynamics from noisy measurements can be achieved more effectively with a Bayesian filtering approach than traditional methods.
By enforcing physical laws, Lagrangian Neural Networks can significantly improve the accuracy and generalization of dynamics models within Model-Based Reinforcement Learning.
