Paper Citation Record · LEDGER

Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1708.06850.

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

pith.paper-citation-record.v1
1708.06850 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:46:47.308849Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:11:12.858279Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 50e59caa-7b9f-45a0-b60b-09f9073c326b · inbound

Koopman Representations of Dynamic Systems with Control cites this paper.

Koopman Representations of Dynamic Systems with Control Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T15:00:09.961607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f24dc2d-ac08-4546-b6b1-a84d4e891c33 · inbound

Generative stochastic modeling of strongly nonlinear flows with non-Gaussian statistics cites this paper.

Generative stochastic modeling of strongly nonlinear flows with non-Gaussian statistics Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-14T12:20:52.788340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0755e3a-759e-4a62-9cf6-0baad16224d9 · inbound

Adaptive Physics-Informed System Modeling with Control for Nonlinear Structural System Estimation cites this paper.

Adaptive Physics-Informed System Modeling with Control for Nonlinear Structural System Estimation Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:47.308849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 352db564-9099-4673-a907-1c9f4b48d293 · inbound

Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators cites this paper.

Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-07-04T22:23:57.650685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.