Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and Normalizing Flows
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2503.03977.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T22:43:00.551396Z
A source-named dated measurement, never combined with another source.
Source: cited_works
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 1ea5376a-ece7-4ab2-b75e-98be37932b22 · inbound
Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach cites this paper.
Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and Normalizing Flows
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T22:43:00.551396Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.