Paper Citation Record · LEDGER

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics

As of 19 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 2 inbound Pith citation observations for arXiv:2505.20370.

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

pith.paper-citation-record.v1
2505.20370 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T20:41:28.131651Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T20:43:43.395511Z

Reference resolution

60 of 60 outbound references displayed

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External citation measurements

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Outbound references

Observation 41c5b570-652e-44c0-87a4-59878d053b9d · outbound

This paper cites Hamiltonian neural networks.Advances in neural information processing systems, 32, 2019.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Hamiltonian neural networks.Advances in neural information processing systems, 32, 2019

Reference 1

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Observation 713d2c43-4b40-4250-a2a8-b577c2d2d57c · outbound

This paper cites Lagrangian Neural Networks.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Lagrangian Neural Networks

Reference 2

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Observation 5c4eea68-2252-4040-9663-668a8f97d536 · outbound

This paper cites Generalized Lagrangian Neural Networks.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Generalized Lagrangian Neural Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:42.940900Z

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Observation 5536a2bf-561f-49f0-a01a-b1c951e88fac · outbound

This paper cites Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:42.993127Z

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Observation c1533861-ebaa-41ef-ae07-82fb38be4ea3 · outbound

This paper cites Solving inverse problems using data-driven models.Acta Numerica, 28:1–174, 2019.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Solving inverse problems using data-driven models.Acta Numerica, 28:1–174, 2019

Reference 5

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Observation a0e2d57f-ec2a-4801-80da-29b3bc9dd166 · outbound

This paper cites Data-driven modeling: concept, techniques, challenges and a case study.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Data-driven modeling: concept, techniques, challenges and a case study

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:55.247266Z

Source-reported events for the cited work

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Observation f00e3e5f-4e88-4d7a-87ad-c7ee2164788a · outbound

This paper cites A review on basic data-driven approaches for industrial process monitoring.IEEE Transactions on Industrial electronics, 61(11):6418– 6428, 2014.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics A review on basic data-driven approaches for industrial process monitoring.IEEE Transactions on Industrial electronics, 61(11):6418– 6428, 2014

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:55.122673Z

Source-reported events for the cited work

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Observation 618eebbc-f6c7-4e0a-89e9-ff17da70be10 · outbound

This paper cites Dealing with noise problem in machine learning data-sets: A systematic review.Procedia Computer Science, 161:466–474, 2019.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Dealing with noise problem in machine learning data-sets: A systematic review.Procedia Computer Science, 161:466–474, 2019

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:43.259126Z

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Observation e6f29753-3798-49be-b175-a11d0cf9fe9e · outbound

This paper cites Generalizing to unseen domains: A survey on domain general- ization.IEEE transactions on knowledge and data engineering, 35(8):8052–8072, 2022.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Generalizing to unseen domains: A survey on domain general- ization.IEEE transactions on knowledge and data engineering, 35(8):8052–8072, 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:54.985463Z

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Observation 0c02642b-c1dc-4333-856d-4b399a554b6c · outbound

This paper cites Numerical differentiation of noisy, nonsmooth data.International Scholarly Research Notices, 2011(1):164564, 2011.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Numerical differentiation of noisy, nonsmooth data.International Scholarly Research Notices, 2011(1):164564, 2011

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:54.846768Z

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Observation 4250c996-86a3-4e94-b931-a30a82ae1353 · outbound

This paper cites an unresolved cited work.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:11:54.735327Z

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Observation 664a65da-87d1-4cc4-9f58-eea936423732 · outbound

This paper cites A survey of advances in vision-based human motion capture and analysis.Computer vision and image understanding, 104(2-3): 90–126, 2006.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics A survey of advances in vision-based human motion capture and analysis.Computer vision and image understanding, 104(2-3): 90–126, 2006

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:54.533230Z

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Observation 972fcb47-2870-47fc-ba76-582f17ab34c6 · outbound

This paper cites Skeleton-based abnormal gait detection.Sensors, 16(11):1792, 2016.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Skeleton-based abnormal gait detection.Sensors, 16(11):1792, 2016

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:54.316062Z

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Observation aec1214f-9545-4878-8a5d-0d97ef498695 · outbound

This paper cites Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review.Computers in Biology and Medicine, 158:106835, 2023.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review.Computers in Biology and Medicine, 158:106835, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:54.064712Z

Source-reported events for the cited work

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Observation 94684fd4-eed4-45a0-8089-f89a76fe7d11 · outbound

This paper cites An attention enhanced spatial– temporal graph convolutional lstm network for action recognition in karate.Applied Sciences, 11(18):8641, 2021.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics An attention enhanced spatial– temporal graph convolutional lstm network for action recognition in karate.Applied Sciences, 11(18):8641, 2021

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:53.874613Z

Source-reported events for the cited work

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Observation c1b5d876-557d-465e-9271-73c971de4f2c · outbound

This paper cites Variational learning of euler–lagrange dynamics from data.Journal of Computational and Applied Mathematics, 421:114780, 2023.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Variational learning of euler–lagrange dynamics from data.Journal of Computational and Applied Mathematics, 421:114780, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:53.667489Z

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Observation f21eda87-3335-4d04-8afa-aeb8e96b3bd7 · outbound

This paper cites Port-hamiltonian neural networks for learning explicit time-dependent dynamical systems.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Port-hamiltonian neural networks for learning explicit time-dependent dynamical systems

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:53.480553Z

Source-reported events for the cited work

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Observation dec3f621-f015-4a2c-a958-509d9dfc8c8f · outbound

This paper cites Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 18

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Observation d9d13627-364f-4d0b-bf63-23762d538f03 · outbound

This paper cites Neural ordinary differential equations.Advances in neural information processing systems, 31, 2018.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Neural ordinary differential equations.Advances in neural information processing systems, 31, 2018

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:44.470237Z

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Observation 703bac9e-bdce-4576-bf27-965ae7137bcb · outbound

This paper cites Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring.Expert Systems with Applications, page 124678, 2024.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring.Expert Systems with Applications, page 124678, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:53.253870Z

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Observation d49a81fa-c384-4c70-960a-809b84ec9ecd · outbound

This paper cites Deep learning and process understanding for data-driven earth system science.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Deep learning and process understanding for data-driven earth system science

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:53.042671Z

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Observation 81e488a1-f881-42e9-8391-648a075a43b0 · outbound

This paper cites Discovering physical concepts with neural networks.Physical review letters, 124(1):010508, 2020.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Discovering physical concepts with neural networks.Physical review letters, 124(1):010508, 2020

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:52.854571Z

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Observation 11c124ca-bbd6-4d1d-a692-5e25f8e30b13 · outbound

This paper cites Theory-guided data science: A new paradigm for scientific discovery from data.IEEE Transactions on knowledge and data engineering, 29(10):2318–2331, 2017.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Theory-guided data science: A new paradigm for scientific discovery from data.IEEE Transactions on knowledge and data engineering, 29(10):2318–2331, 2017

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:52.700627Z

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Observation a07b7b87-86d4-4985-ba76-681953683d53 · outbound

This paper cites Physics-informed machine learning for modeling multidimensional dynamics.Nonlinear Dynamics, 112(24): 21565–21585, 2024.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Physics-informed machine learning for modeling multidimensional dynamics.Nonlinear Dynamics, 112(24): 21565–21585, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:52.522760Z

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Observation f754f8e1-7bb2-4d3a-b64d-3dfe9e1d26ce · outbound

This paper cites an unresolved cited work.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:11:52.318218Z

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Observation 351996ce-a21f-480a-ad1d-abc9a80173a4 · outbound

This paper cites Deep energy-based modeling of discrete-time physics.Advances in Neural Information Processing Systems, 33:13100–13111, 2020.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Deep energy-based modeling of discrete-time physics.Advances in Neural Information Processing Systems, 33:13100–13111, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:52.101197Z

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Observation a691cb27-71d2-4b0d-9c78-274ac00c5e8f · outbound

This paper cites Neural symplectic form: Learn- ing hamiltonian equations on general coordinate systems.Advances in Neural Information Processing Systems, 34:16659–16670, 2021.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Neural symplectic form: Learn- ing hamiltonian equations on general coordinate systems.Advances in Neural Information Processing Systems, 34:16659–16670, 2021

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:51.878524Z

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Observation e4c8359c-81ca-48a7-881b-5b4194b2c7c5 · outbound

This paper cites Physics-informed neural ode (pinode): embedding physics into models using collocation points.Scientific Reports, 13 (1):10166, 2023.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Physics-informed neural ode (pinode): embedding physics into models using collocation points.Scientific Reports, 13 (1):10166, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:51.643185Z

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Observation cd0e4e11-c411-44ef-96e6-e5c268d45986 · outbound

This paper cites Learning dynamical systems from noisy data with inverse-explicit integrators.Physica D: Nonlinear Phenomena, 472:134471, 2025.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Learning dynamical systems from noisy data with inverse-explicit integrators.Physica D: Nonlinear Phenomena, 472:134471, 2025

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:51.521597Z

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Observation e31c27d6-91d5-458a-9c43-4c1f976238a5 · outbound

This paper cites Symplectic Neural Networks Based on Dynamical Systems.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Symplectic Neural Networks Based on Dynamical Systems

Reference 30

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Source-reported events for the cited work

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Observation 84d4b2fb-6e8e-4d88-9666-382e7259d39b · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932–3937, 2016.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932–3937, 2016

Reference 32

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Observation b5b4ea81-f305-4b1d-aa99-8b04c88e6190 · outbound

This paper cites Data-driven discovery of coordinates and governing equations.Proceedings of the National Academy of Sciences, 116(45):22445–22451, 2019.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Data-driven discovery of coordinates and governing equations.Proceedings of the National Academy of Sciences, 116(45):22445–22451, 2019

Reference 33

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Observation 55701172-7579-4eeb-b240-7e05fd8df277 · outbound

This paper cites Simplifying hamiltonian and lagrangian neural networks via explicit constraints.Advances in neural information processing systems, 33:13880–13889, 2020.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Simplifying hamiltonian and lagrangian neural networks via explicit constraints.Advances in neural information processing systems, 33:13880–13889, 2020

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:51.348203Z

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Observation 3b368441-03d9-4df9-a031-42feabde056b · outbound

This paper cites Learning hamiltonians of constrained mechanical systems.Journal of Computational and Applied Mathematics, 417: 114608, 2023.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Learning hamiltonians of constrained mechanical systems.Journal of Computational and Applied Mathematics, 417: 114608, 2023

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:51.225647Z

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Observation 22a3ace0-269e-44c5-ba83-483f1fca2e83 · outbound

This paper cites A Structure-Preserving Kernel Method for Learning Hamiltonian Systems.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics A Structure-Preserving Kernel Method for Learning Hamiltonian Systems

Reference 36

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Observation 7a9a1d14-6a0e-484f-9040-b24752396161 · outbound

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Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:11:51.098702Z

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Observation 3afda72d-0966-4919-9999-f734b9985ec6 · outbound

This paper cites an unresolved cited work.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:11:50.955901Z

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Observation 5bdd4915-5f5b-4ec8-9d93-5de794b58731 · outbound

This paper cites Learning strange attractors with reservoir systems.Nonlinearity, 36(9):4674, 2023.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Learning strange attractors with reservoir systems.Nonlinearity, 36(9):4674, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:50.832144Z

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Observation 36800393-84b7-41ed-b21b-5991f5347ae8 · outbound

This paper cites Deep discrete-time lagrangian mechanics.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Deep discrete-time lagrangian mechanics

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:50.673145Z

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Observation bf9ee673-a56a-432d-a242-c633642f401b · outbound

This paper cites Machine learning and serving of discrete field theories.Scientific Reports, 10(1): 19329, 2020.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Machine learning and serving of discrete field theories.Scientific Reports, 10(1): 19329, 2020

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:50.542549Z

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Observation 0c28b7fd-64b7-4dec-950f-b71bdb26178a · outbound

This paper cites Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Reference 42

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Observation c2a6cfdd-2963-4d8c-914a-22bcffc71320 · outbound

This paper cites Learning of discrete models of variational pdes from data.Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(1), 2024.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Learning of discrete models of variational pdes from data.Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(1), 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:50.386049Z

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Observation 5d1f05db-4c70-4f2b-b557-a9b6a56048ff · outbound

This paper cites Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:11:48.450182Z

Source-reported events for the cited work

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Observation 8e599a2a-b9af-42b4-b064-8bbd71d22936 · outbound

This paper cites Learnability of linear port-hamiltonian systems.Journal of Machine Learning Research, 25(68):1–56, 2024.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Learnability of linear port-hamiltonian systems.Journal of Machine Learning Research, 25(68):1–56, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:50.256746Z

Source-reported events for the cited work

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Observation 279815c1-be4e-459e-a659-f54bf64820b3 · outbound

This paper cites Pseudo-hamiltonian neural networks for learning partial differential equations.Journal of Computational Physics, 500:112738, 2024.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Pseudo-hamiltonian neural networks for learning partial differential equations.Journal of Computational Physics, 500:112738, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:50.093861Z

Source-reported events for the cited work

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Observation 250eb961-934c-44f9-8438-58a368b39384 · outbound

This paper cites Efficiently Parameterized Neural Metriplectic Systems.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Efficiently Parameterized Neural Metriplectic Systems

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:46.895215Z

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Observation aca81a43-5ad2-4695-ad0a-e8674b50b511 · outbound

This paper cites Variational order for forced lagrangian systems.Nonlinearity, 31(8):3814, 2018.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Variational order for forced lagrangian systems.Nonlinearity, 31(8):3814, 2018

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:49.987726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4f279685-a7e5-4425-8832-0a2793546d9a · outbound

This paper cites an unresolved cited work.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-07T14:11:49.870772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f5ff3d8d-d69d-40d4-81a3-17e0a15cd11b · outbound

This paper cites Discrete lagrangian neural networks with automatic symmetry discovery.IFAC-PapersOnLine, 56(2):3203–3210, 2023.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Discrete lagrangian neural networks with automatic symmetry discovery.IFAC-PapersOnLine, 56(2):3203–3210, 2023

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:49.733048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 35e90f4b-9222-4f98-9545-89bf66937d25 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:47.245707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75e0abdf-693b-4579-bbc5-0ff6617a8dca · outbound

This paper cites an unresolved cited work.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:11:49.614745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3506918f-5911-45ba-9505-2dd5bf8140a4 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:47.459099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5274fb3-227f-47cc-a48d-b5f17d9c7e97 · outbound

This paper cites OpenAI Gym.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics OpenAI Gym

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:47.540739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b127d27-7b5d-448b-ba3d-9ce2e1221364 · outbound

This paper cites A combined corner and edge detector.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics A combined corner and edge detector

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:49.521911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d644cf2-be37-457f-991f-a1a60090cdfb · outbound

This paper cites Measures of the amount of ecologic association between species.Ecology, 26(3): 297–302, 1945.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Measures of the amount of ecologic association between species.Ecology, 26(3): 297–302, 1945

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:49.408909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 635108a3-a404-4463-b1bf-fa1bf53b0f2a · outbound

This paper cites Cmu graphics lab motion capture database.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Cmu graphics lab motion capture database

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:49.305861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44e1bf45-4251-4e6a-8091-1a14e3bc18f4 · outbound

This paper cites Guide to the carnegie mellon university multimodal activity (cmu- mmac) database.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Guide to the carnegie mellon university multimodal activity (cmu- mmac) database

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:49.187501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9adf444c-438a-49ed-948d-ba77468e4502 · outbound

This paper cites Smoothing and differentiation of data by simplified least squares procedures.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Smoothing and differentiation of data by simplified least squares procedures

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:49.050837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 76a8e2a7-4580-4b6c-a52e-aacdf43b6c42 · outbound

This paper cites Symmetric multistep methods over long times.Numerische Mathematik, 97:699–723, 2004.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Symmetric multistep methods over long times.Numerische Mathematik, 97:699–723, 2004

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:48.937312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5111ac2-2045-480f-99ea-1cde823527a3 · outbound

This paper cites Symmetric multistep methods for charged-particle dynamics.

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics Symmetric multistep methods for charged-particle dynamics

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:48.778596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation cbae8a6f-8721-493b-ad2a-a2b9f205e83d · inbound

Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations cites this paper.

Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:56:05.814368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2e8a4f45-96b3-48c0-8c7b-365169e40d83 · inbound

Hypothesis-driven construction of mesoscopic dynamics cites this paper.

Hypothesis-driven construction of mesoscopic dynamics Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:43:43.397035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.