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.
Survey of maneuvering target tracking. Part I: Dynamic models,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.