Paper Citation Record · LEDGER

Learning Dissipative Dynamics in Chaotic Systems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.06898.

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

pith.paper-citation-record.v1
2106.06898 v2

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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:46:04.894042Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:24:00.371157Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 04575ce3-a6c6-4496-bbdc-1f5d820a16cd · inbound

A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results cites this paper.

A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results Learning Dissipative Dynamics in Chaotic Systems

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:48:32.869820Z

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.

Observation 5bcdfdc9-ebdc-48ed-86ab-718b3538c54f · inbound

Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage cites this paper.

Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage Learning Dissipative Dynamics in Chaotic Systems

Reference 111

Resolution: verified exact; arxiv_id, observed 2026-05-19T02:36:59.566227Z

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.

Observation 77f58ecf-3251-40ff-bc90-361ea159008a · inbound

Modeling turbulent and self-gravitating fluids with Fourier neural operators cites this paper.

Modeling turbulent and self-gravitating fluids with Fourier neural operators Learning Dissipative Dynamics in Chaotic Systems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T10:46:04.894042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf025846-0da0-41b9-a22f-12a45ac29da6 · inbound

Is Flow Matching Just Trajectory Replay for Sequential Data? cites this paper.

Is Flow Matching Just Trajectory Replay for Sequential Data? Learning Dissipative Dynamics in Chaotic Systems

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:22:27.408681Z

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.

Observation 58088a01-f7f2-4d61-a34e-6f96a5027ec3 · inbound

Semigroup Consistency as a Diagnostic for Learned Physics Simulators cites this paper.

Semigroup Consistency as a Diagnostic for Learned Physics Simulators Learning Dissipative Dynamics in Chaotic Systems

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:24:00.373020Z

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.

Observation cede69e2-cd4e-4be2-88ec-716a3018f92e · inbound

Explainable quantum-compressed machine learning for complex fluid flows cites this paper.

Explainable quantum-compressed machine learning for complex fluid flows Learning Dissipative Dynamics in Chaotic Systems

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T07:36:54.327367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.