Paper Citation Record · LEDGER

Efficiently Parameterized Neural Metriplectic Systems

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.16305.

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

pith.paper-citation-record.v1
2405.16305 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:11:54.126575Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T20:37:34.124812Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 0441ed08-aa69-4989-8f94-bd5ef9869745 · inbound

SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty cites this paper.

SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty Efficiently Parameterized Neural Metriplectic Systems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:11:54.126575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 250eb961-934c-44f9-8438-58a368b39384 · 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 Efficiently Parameterized Neural Metriplectic Systems

Reference 47

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c421aac-b75b-4aae-91ad-cd8477ad90b1 · inbound

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

Rapid training of Hamiltonian graph networks using random features Efficiently Parameterized Neural Metriplectic Systems

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:17:15.646816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eb838199-2899-4831-9921-f94b5294310e · inbound

Structure-Preserving Digital Twins via Conditional Neural Whitney Forms cites this paper.

Structure-Preserving Digital Twins via Conditional Neural Whitney Forms Efficiently Parameterized Neural Metriplectic Systems

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T22:33:49.771814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f86a7fe3-9a2a-4d58-ae49-b88358575cb1 · 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 Efficiently Parameterized Neural Metriplectic Systems

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:37:34.126443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ca65525-da85-4596-90f9-5ddf286d7294 · 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 Efficiently Parameterized Neural Metriplectic Systems

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-02T08:18:18.314766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.