Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1906.08829.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T13:35:46.029397Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T10:32:13.322525Z
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 1f709891-f3e2-4572-8363-4b029eb1da5b · inbound
Embedding and Approximation Theorems for Echo State Networks cites this paper.
Embedding and Approximation Theorems for Echo State Networks Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:35:46.029397Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a2d4b553-d67b-4683-a39d-4cbe92cbe797 · inbound
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings cites this paper.
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:07:34.531821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31a91374-063d-4f33-83fb-ca96d63d9132 · inbound
Kernel Methods for the Approximation of the Eigenfunctions of the Koopman Operator cites this paper.
Kernel Methods for the Approximation of the Eigenfunctions of the Koopman Operator Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T10:32:13.327678Z
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.