Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.20603.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T22:50:31.128208Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 456365fb-226e-46d0-9033-964bb17edbb1 · inbound
When is a System Discoverable from Data? Discovery Requires Chaos cites this paper.
When is a System Discoverable from Data? Discovery Requires Chaos Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
Reference 148
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:50:31.128208Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ff26e8a-fc70-4aee-88b8-a1a761c542f8 · inbound
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction cites this paper.
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
Reference 105
- Resolution
- verified exact
- arxiv_id, observed 2026-06-26T18:29:41.718752Z
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 494e465e-f7c2-4020-8094-e9d153b4e0e6 · inbound
Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks cites this paper.
Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:16:49.146457Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.