Paper Citation Record · LEDGER

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:1909.12077.

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

pith.paper-citation-record.v1
1909.12077 v5

Coverage vector

measured 0 of 0 reference resolution

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measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:10:26.705964Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T20:37:34.130524Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9730ac11-cf0a-413b-98aa-c866b0620b2d · inbound

Universal Differential Equations for Scientific Machine Learning cites this paper.

Universal Differential Equations for Scientific Machine Learning Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:24:43.455082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5fd6068d-2cff-4467-acdb-6665a628345f · inbound

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network cites this paper.

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:10:26.705964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a78efac-de59-4223-8bfd-fb6377572ea1 · inbound

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling cites this paper.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T12:10:08.635352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30f442bf-4934-4ee4-9ad8-fd72bff219a9 · inbound

Detecting Deepfakes via Hamiltonian Dynamics cites this paper.

Detecting Deepfakes via Hamiltonian Dynamics Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:50:39.857716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c17461e-353d-4e79-b30e-c45682fd9563 · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:06:27.301155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 712d6adb-a890-42d4-a639-0b9bbe298dfc · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:52:59.173509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 129d5d9c-c393-4de5-bdd3-c6b24d4b51ba · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T22:23:48.185270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26e19bad-d012-42d1-84c0-c8b1fffaabfe · inbound

Learning partially observed systems with neural Hamiltonian ordinary differential equations cites this paper.

Learning partially observed systems with neural Hamiltonian ordinary differential equations Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:10:22.179403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23c035dc-4d89-4129-8e5f-9bd1d554c3a3 · inbound

A comparative study of accuracy and rollout stability of temporal surrogate models cites this paper.

A comparative study of accuracy and rollout stability of temporal surrogate models Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:38.554373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9879a673-25df-4e6e-9654-68a413b35796 · inbound

Robots Need More than VLA and World Models cites this paper.

Robots Need More than VLA and World Models Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:46:59.291620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab802362-6a72-4619-9275-463f9a0b58e7 · inbound

NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics cites this paper.

NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:18:43.374509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c43eb8c8-934c-4349-ae8a-ad58a000e81c · inbound

Locally Stable Neural ODEs with Characterized Region of Attraction cites this paper.

Locally Stable Neural ODEs with Characterized Region of Attraction Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:09:22.482213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0fb2c0b-66a6-4481-8dcc-63a968214e9f · inbound

Symplectic Neural Networks for Learning Non-Separable Hamiltonians cites this paper.

Symplectic Neural Networks for Learning Non-Separable Hamiltonians Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:09:50.201995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8c1a3289-dc4c-40b5-8559-fc70a505410e · inbound

CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts cites this paper.

CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:37:34.131882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8763cb6-2f5a-48b7-8314-8ac46e112ba7 · inbound

Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity cites this paper.

Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T06:43:07.204204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.