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Paper Citation Record · LEDGER

Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2201.10085.

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

pith.paper-citation-record.v1
2201.10085 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:11:44.400244Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:18:43.375754Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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

Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics cites this paper.

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

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T14:11:44.400244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ef120147-2bc4-4ea1-aa9b-e0820efcfbfc · inbound

Rapid training of Hamiltonian graph networks using random features cites this paper.

Rapid training of Hamiltonian graph networks using random features Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 83

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verified exact
arxiv_id, observed 2026-05-19T10:17:15.686360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T10:17:00.343410Z digest=sha256:c5a48f8b7f3c5808fffad7c41020d594526330f00855cec3c2583f311d641066

Observation 0b5c878e-8c57-4ecb-b442-24f4adc63425 · inbound

SlotPi: Physics-informed Object-centric Reasoning Models cites this paper.

SlotPi: Physics-informed Object-centric Reasoning Models Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 74

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no resolver link, observed 2026-08-07T04:25:21.178217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:25:21.178217Z digest=sha256:b4be370743da61e678bcc1585a2667684178555b0258797c8201b80c659b2f28

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

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data cites this paper.

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.

source=pdf_text observed=2026-08-04T22:37:31.490871Z digest=sha256:ab0e25fb72c93200afafcf844aae5873e52f2ef10a669bef041cc35d3436e08c

Observation d3e0a14b-bc25-4691-aac3-ba71fec69b4b · inbound

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach cites this paper.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-04T13:54:15.995251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T13:54:15.995251Z digest=sha256:ed3f669a4ff46f555f434845878353b5b5e9e8eadd27b4189905c351cfcba968

Observation 875b5c81-6a2b-455e-85bd-37aa20bd1097 · inbound

Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches cites this paper.

Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T18:15:37.494755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:15:37.494755Z digest=sha256:5cff9935c37ff5bdfea60e898a0d98c51df476948a40a96e7ccd08938a051a5f

Observation ab867042-a9cc-4179-a5ce-58f9d8799469 · inbound

Metriplector: From Field Theory to Neural Architecture cites this paper.

Metriplector: From Field Theory to Neural Architecture Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-13T23:58:28.691159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T23:57:39.264033Z digest=sha256:96670b07cdd2686488ec37e2348443114a7ad364590b04ad1e179ce51710e3fc

Observation 5f2dd005-41d3-4fa8-beb5-068db2364c35 · inbound

Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data cites this paper.

Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:06:53.000750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T07:03:57.353828Z digest=sha256:68f64307b0204087b9658ba6ef126b94c06510080a2382ed52cbe228e92fab23

Observation f7eb1951-4291-4e6d-bd08-11e740347852 · inbound

Predicting and controlling nonlinear neuro-mechanical locomotion dynamics cites this paper.

Predicting and controlling nonlinear neuro-mechanical locomotion dynamics Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:46:32.594206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-07T12:40:05.135887Z digest=sha256:fad480ccdb5fccf6d0c41e8d43231c3c3e4c1586aa499e64507a4e7ac0f0bfe7

Observation 198a5156-258a-4a79-ae77-deea5c886129 · inbound

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

Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:56:05.803812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 7efbe9df-ecc2-4819-935c-f2e5be1bd6fa · 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 Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-07-03T17:18:43.377502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T04:25:27.067362Z digest=sha256:73f2187728a3a5d472ac4c5e901c13b5285f80e0505dc5d0a32387e42a06afcc

Observation ad03f8b6-21ab-4778-aa71-134eeab71c54 · 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 Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-02T08:18:18.644747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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