Paper Citation Record · LEDGER

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2606.31137.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.31137 v1

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Outbound references

Observation a57a80d0-b76a-4065-ae92-91748b138268 · outbound

This paper cites Siciliano, O.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Siciliano, O

Reference 1

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This paper cites Rigatos,Modelling and Control for Intelligent Industrial Systems: Adaptive Algorithms in Robotics and Industrial Engineering.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Rigatos,Modelling and Control for Intelligent Industrial Systems: Adaptive Algorithms in Robotics and Industrial Engineering

Reference 2

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A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Unresolved cited work

Reference 3

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This paper cites Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER,

Reference 4

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This paper cites S ¨arkk¨a and A.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements S ¨arkk¨a and A

Reference 5

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This paper cites Survey of maneuvering target tracking. Part I: Dynamic models,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Survey of maneuvering target tracking. Part I: Dynamic models,

Reference 6

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Observation 9eee1cdb-54e6-4f00-a47d-89b763227e80 · outbound

This paper cites Identification and control of dynamical systems using neural networks,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Identification and control of dynamical systems using neural networks,

Reference 7

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This paper cites Neural networks for system identification,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Neural networks for system identification,

Reference 8

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This paper cites Nonlinear state space model identification using a regularized basis function expansion,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Nonlinear state space model identification using a regularized basis function expansion,

Reference 9

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This paper cites Computationally efficient Bayesian learning of Gaussian process state space models,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Computationally efficient Bayesian learning of Gaussian process state space models,

Reference 10

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Observation b854b14d-16be-4f98-b557-d249cb2524c1 · outbound

This paper cites The use of Gaussian processes in system identification,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements The use of Gaussian processes in system identification,

Reference 11

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Observation c4eef3d8-11b6-417b-9b0b-fb2b2d3c1bca · outbound

This paper cites Physics-informed machine learning.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Physics-informed machine learning

Reference 12

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Observation 44dd5617-7cbd-42ed-94b1-9c7bceb3ed4b · outbound

This paper cites Physics- informed neural networks (PINNs) for fluid mechanics: A review,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Physics- informed neural networks (PINNs) for fluid mechanics: A review,

Reference 13

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This paper cites Hamiltonian neural net- works,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Hamiltonian neural net- works,

Reference 14

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Observation fcecec5d-2703-44fe-a4b8-0a58900d6746 · outbound

This paper cites Lagrangian neural networks,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Lagrangian neural networks,

Reference 15

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Observation 93a1a33f-b616-4017-9ee9-0ef901b7fdcd · outbound

This paper cites Deep Lagrangian networks: Using physics as model prior for deep learning,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Deep Lagrangian networks: Using physics as model prior for deep learning,

Reference 16

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This paper cites Deep Lagrangian networks for end-to-end learning of energy-based control for under-actuated systems,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Deep Lagrangian networks for end-to-end learning of energy-based control for under-actuated systems,

Reference 17

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This paper cites Combining physics and deep learning to learn continuous-time dynamics models,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Combining physics and deep learning to learn continuous-time dynamics models,

Reference 18

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This paper cites Integrating Lagrangian neural networks into the Dyna framework for reinforcement learning,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Integrating Lagrangian neural networks into the Dyna framework for reinforcement learning,

Reference 19

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A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Unresolved cited work

Reference 20

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This paper cites Bar-Shalom, X.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Bar-Shalom, X

Reference 21

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Observation 383a6ce8-a18a-4d4f-b672-8936de5acc3b · outbound

This paper cites S ¨arkk¨a and L.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements S ¨arkk¨a and L

Reference 22

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Observation afe50e05-4480-43ea-9048-cc18368b6b73 · outbound

This paper cites Cubature Kalman filters.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Cubature Kalman filters

Reference 23

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This paper cites The unscented Kalman filter for nonlinear estimation,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements The unscented Kalman filter for nonlinear estimation,

Reference 24

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This paper cites Gaussian filters for nonlinear filtering problems,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Gaussian filters for nonlinear filtering problems,

Reference 25

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This paper cites Goldstein, C.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Goldstein, C

Reference 26

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A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Unresolved cited work

Reference 27

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This paper cites Comment on “A new method for the nonlinear transformation of means and covariances in filters and estimators.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Comment on “A new method for the nonlinear transformation of means and covariances in filters and estimators

Reference 28

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Observation 0f5b7445-6687-4b59-970e-ee3fed8a288d · outbound

This paper cites Posterior linearization filter: Principles and implementation using sigma points,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Posterior linearization filter: Principles and implementation using sigma points,

Reference 29

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This paper cites Iterative filtering and smoothing in nonlinear and non-Gaussian systems using conditional moments,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Iterative filtering and smoothing in nonlinear and non-Gaussian systems using conditional moments,

Reference 30

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This paper cites Discrete-time nonlinear filtering algorithms using Gauss–Hermite quadrature,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Discrete-time nonlinear filtering algorithms using Gauss–Hermite quadrature,

Reference 31

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This paper cites Sigma-point filtering and smooth- ing based parameter estimation in nonlinear dynamic systems,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Sigma-point filtering and smooth- ing based parameter estimation in nonlinear dynamic systems,

Reference 32

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This paper cites Goodfellow, Y.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements Goodfellow, Y

Reference 33

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This paper cites State identification of Duffing oscillator based on extreme learning machine,.

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements State identification of Duffing oscillator based on extreme learning machine,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T16:42:40.272003Z

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