Paper Citation Record · LEDGER

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2509.07280.

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

pith.paper-citation-record.v1
2509.07280 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:37:31.550964Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:03:58.907980Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

31 of 31 outbound references displayed

verified exact3
verified fuzzy21
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 9ed4f22e-a7c5-49c2-8eb3-290cc6ba221e · outbound

This paper cites Learning unknown ODE models with Gaussian processes.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Learning unknown ODE models with Gaussian processes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.869496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d63f04e9-6c6f-4e69-adc6-88d3f5a13d57 · outbound

This paper cites Neural ordinary differential equations.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Neural ordinary differential equations

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.861168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7304dc9-0a63-443c-ad46-a4d206e87068 · outbound

This paper cites Learning Dynamical Systems from Partial Observations.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Learning Dynamical Systems from Partial Observations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T22:37:31.469369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f98b6d17-a6ae-4fa1-8d13-adbfee102ed3 · outbound

This paper cites Learning stable deep dynamics models.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Learning stable deep dynamics models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.852333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b7148d3-a78f-48ae-aa34-790511c6f986 · outbound

This paper cites Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T22:37:31.475945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a88b180-ff09-4e49-9544-ebd2074435f3 · outbound

This paper cites Neural sdes as infinite-dimensional gans.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Neural sdes as infinite-dimensional gans

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.842983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 875e4eb8-0184-4e79-aa5f-73dd0aa97919 · outbound

This paper cites Hamiltonian neural networks.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Hamiltonian neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.833919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8b28d04-6f36-4dc9-83d0-a7518333a487 · outbound

This paper cites Symplectic Gaussian process regression of maps in Hamiltonian systems.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Symplectic Gaussian process regression of maps in Hamiltonian systems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.824775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5e9190e9-6b0c-4c67-9006-b02efbad5cd5 · outbound

This paper cites Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:37:31.650602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 52b13849-49c3-4152-97ec-d032e3249dbd · outbound

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

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T22:37:31.490871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3241a5e-be67-40c3-a515-b7da5a38d039 · outbound

This paper cites Dissipative SymODEN: encoding Hamiltonian dynamics with sissipation and control into deep learning.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Dissipative SymODEN: encoding Hamiltonian dynamics with sissipation and control into deep learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.815495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation db6636bf-a7dc-445a-a7da-ec21c4ea51de · outbound

This paper cites Symplectic spectrum Gaussian processes: learning Hamiltonians from noisy and sparse data.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Symplectic spectrum Gaussian processes: learning Hamiltonians from noisy and sparse data

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.805872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e44b99a3-c5f7-4c69-a3e8-ff5419679d4f · outbound

This paper cites Port-Hamiltonian systems theory: an introductory overview.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Port-Hamiltonian systems theory: an introductory overview

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.796794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6025b20-813d-4814-b5c1-81b6496735fc · outbound

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

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.787099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 135bf037-7e44-48cf-b819-632de1c6dcdc · outbound

This paper cites Hamiltonian systems.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Hamiltonian systems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.774858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0c599b82-60c4-4a22-8451-9b1c4a61385f · outbound

This paper cites Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.765807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd5a5f03-a829-456b-b3c5-d4a47e665b8d · outbound

This paper cites Random features for large-scale kernel machines.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Random features for large-scale kernel machines

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T22:37:31.510389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab9ad126-313d-4543-8eae-de4661ae35ec · outbound

This paper cites Sparse spectrum Gaussian process regression.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Sparse spectrum Gaussian process regression

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.750623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1ba0261b-9852-4ba9-8600-1f51b03a46d7 · outbound

This paper cites Spherical structured feature maps for kernel approximation.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Spherical structured feature maps for kernel approximation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.741849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 528f93db-86e1-42a6-9be7-83c3953637bb · outbound

This paper cites Gaussian process port-Hamiltonian systems: Bayesian learning with physics prior.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Gaussian process port-Hamiltonian systems: Bayesian learning with physics prior

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-08-04T22:37:31.629606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9919b14-36f7-4275-89bd-928a74ac8516 · outbound

This paper cites LyaNet: a Lyapunov framework for training neural odes.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data LyaNet: a Lyapunov framework for training neural odes

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.733532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 68eab5d2-387f-4cbf-ae30-cc20a0055aa1 · outbound

This paper cites The Onsager-Machlup function as Lagrangian for the most probable path of a diffusion process.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data The Onsager-Machlup function as Lagrangian for the most probable path of a diffusion process

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.724516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 63ad77a8-efa4-45d2-9823-5b61c1e72ff9 · outbound

This paper cites Onsager-Machlup functional for stochastic differential equations with time-varying noise.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Onsager-Machlup functional for stochastic differential equations with time-varying noise

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:37:31.616141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7efd099-8a80-466d-9169-dd99950a308b · outbound

This paper cites Machine learning framework for computing the most probable paths of stochastic dynamical systems.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Machine learning framework for computing the most probable paths of stochastic dynamical systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.714987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 25eda640-30a0-4494-976e-f8398828c38e · outbound

This paper cites Trajectory entropy of continuous stochastic processes at equilib- rium.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Trajectory entropy of continuous stochastic processes at equilib- rium

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.706014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af97da2f-be7b-4848-9e1e-82a59d80190d · outbound

This paper cites On gradient descent ascent for nonconvex-concave minimax problems.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data On gradient descent ascent for nonconvex-concave minimax problems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.697035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6bfe550-74fe-4346-967b-acca7e00dec8 · outbound

This paper cites A single-loop smoothed gradient descent-ascent algorithm for nonconvex-concave min-max problems.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data A single-loop smoothed gradient descent-ascent algorithm for nonconvex-concave min-max problems

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.687855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d3ffb6a6-f24b-45a1-af9f-124f2666481f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Adam: A Method for Stochastic Optimization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T22:37:31.541355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af479d46-a703-4f84-8406-bd6dba584063 · outbound

This paper cites MTAdam: Automatic Balancing of Multiple Training Loss Terms.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data MTAdam: Automatic Balancing of Multiple Training Loss Terms

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:37:31.593710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c1df2bbb-7967-457b-921d-2261b3459999 · outbound

This paper cites Jacobian Descent for Multi-Objective Optimization.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Jacobian Descent for Multi-Objective Optimization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T22:37:31.547786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 363dd410-f7ee-4dbe-99d1-01f24756642c · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data Pytorch: An imperative style, high-performance deep learning library

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:37:31.678227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation f6ec721a-3667-44c9-be77-a8a2a4fdcd61 · inbound

Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation cites this paper.

Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data

Reference 161

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:05:50.041872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.