Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.11213.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:19:08.213531Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T05:12:33.259869Z
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 52e6ce0e-84c9-4192-9b1c-86b61d79b71b · inbound
Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network cites this paper.
Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks
Reference 36
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-11T05:12:33.264585Z
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 94d3cd27-4f78-4791-b7fe-7dde9e1811f4 · inbound
Data-Driven Structural State Estimation via Multi-Fidelity Gaussian Process Models cites this paper.
Data-Driven Structural State Estimation via Multi-Fidelity Gaussian Process Models Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:19:08.213531Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.