Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.17822.
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 2026-08-11T16:37:37.272725Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T15:15:48.091938Z
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 31d3ce4d-5086-4549-a23c-00bc7f8af6b1 · inbound
Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models cites this paper.
Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T16:37:37.272725Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ee2d262-fe48-4da7-9d2c-262e9134f269 · inbound
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations cites this paper.
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T05:40:55.828191Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 954ef1c9-5a34-432b-8d35-7afe44be66af · inbound
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-B\'enard Convection cites this paper.
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-B\'enard Convection Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
Reference 10
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T15:15:48.093518Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 23520195-897b-4c7a-950b-68c554c22313 · inbound
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-B\'enard Convection cites this paper.
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-B\'enard Convection Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T10:44:21.353061Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.