Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2503.16323.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:54:44.046440Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T09:44:45.959216Z
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 6822a28a-b1da-4970-906e-89d7f5d1dd34 · inbound
Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints cites this paper.
Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:54:44.046440Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d4bbf20-77bc-4068-928e-98f03e457d23 · inbound
Agentic Risk-Aware Set-Based Engineering Design cites this paper.
Agentic Risk-Aware Set-Based Engineering Design NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:17:37.521624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3820b14c-c5ce-4407-a8a1-50fe6f234312 · inbound
In-Context Black-Box Optimization with Unreliable Feedback cites this paper.
In-Context Black-Box Optimization with Unreliable Feedback NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Reference 34
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T18:51:07.388036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 15729aad-ae57-4194-af50-812209ac05f3 · inbound
AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation cites this paper.
AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:29:48.236392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2a1ebac9-9432-4f24-a6b0-83f322344ca2 · inbound
AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation cites this paper.
AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T09:44:45.962220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 919eabc6-a0af-4a8b-8862-5a0c322ad90b · inbound
CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Laws cites this paper.
CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Laws NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T19:51:56.544026Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.