A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements

arXiv cs.LG1mo4 min read

arXiv:2606.31137v1 Announce Type: new Abstract: 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 potential energies with neural networks. The unknown external forces in the Lagrangian formulation are modeled as white Gaussian noise. The corresponding Euler--Lagrange equations then yield a continuous-time stochastic state-space model (SSM) that describes the system d

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A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements

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