Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1903.00033.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:40:56.071071Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-01T17:05:50.225583Z
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 c8b76f33-60f9-4fb6-9978-8dfc778d351f · inbound
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers cites this paper.
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:46:21.124287Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation faa6b0c4-2b88-4a4d-9922-791e2ddfd389 · 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 Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:07:34.522251Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ee1479f9-d7ed-4079-a3d1-00ac70794086 · inbound
A novel hybrid neural network of fluid-structure interaction prediction for two cylinders in tandem arrangement cites this paper.
A novel hybrid neural network of fluid-structure interaction prediction for two cylinders in tandem arrangement Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Reference 827
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:56.071071Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2092dd83-cc7b-44f6-a14f-8a2391f23b25 · inbound
Generalization Capability of Deep Learning for Predicting Drag Reduction in Pulsating Turbulent Pipe Flow with Arbitrary Acceleration and Deceleration cites this paper.
Generalization Capability of Deep Learning for Predicting Drag Reduction in Pulsating Turbulent Pipe Flow with Arbitrary Acceleration and Deceleration Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T13:20:54.378904Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7fa1fede-318f-48dd-ab03-f2df6f760066 · inbound
A Differentiable Programming Framework for Accurate and Stable Reduced-Order Modeling of Chaotic Flows cites this paper.
A Differentiable Programming Framework for Accurate and Stable Reduced-Order Modeling of Chaotic Flows Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-07-01T17:05:50.227021Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.