Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2206.05183.
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 2026-08-15T21:49:43.601207Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T04:50:29.874957Z
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 61d1fa0f-87ba-480d-bb72-d86217ac5065 · inbound
Single-shot prediction of parametric partial differential equations cites this paper.
Single-shot prediction of parametric partial differential equations GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:49:43.601207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6b7874b-5a6d-463c-8691-cdfaad889e27 · inbound
MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data cites this paper.
MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:50:55.475793Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4af3707e-032b-4363-8690-6c3551199955 · inbound
Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature cites this paper.
Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T04:50:29.878829Z
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.