Paper Citation Record · LEDGER

PyDMD: A Python package for robust dynamic mode decomposition

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.07463.

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

pith.paper-citation-record.v1
2402.07463 v1

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 2026-08-11T22:42:45.046587Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T14:44:45.439242Z

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 471c6240-5711-4618-837a-078b9c2011e2 · inbound

Interpreting Temporal Graph Neural Networks with Koopman Theory cites this paper.

Interpreting Temporal Graph Neural Networks with Koopman Theory PyDMD: A Python package for robust dynamic mode decomposition

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:43:19.072255Z

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.

Observation 3c97da89-6359-4bff-bfcf-be528e9572dd · inbound

Online Physics-Informed Dynamic Mode Decomposition: Theory and Applications cites this paper.

Online Physics-Informed Dynamic Mode Decomposition: Theory and Applications PyDMD: A Python package for robust dynamic mode decomposition

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T22:42:45.046587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faf80099-e5f1-4347-8ede-a83b6ebfc76d · inbound

Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms cites this paper.

Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms PyDMD: A Python package for robust dynamic mode decomposition

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:00:50.910914Z

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.

Observation 0af69487-f94d-4aae-beb4-19919a025468 · inbound

Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications cites this paper.

Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications PyDMD: A Python package for robust dynamic mode decomposition

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T14:44:45.441098Z

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.