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Paper Citation Record · LEDGER

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.16323.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2503.16323 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:51:56.544026Z

measured 0 of 1 external citation measurements

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 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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T08:14:37.885340Z digest=sha256:97f8f29396f05024fa0f8df0d2eac9a4310ceafd326bedf2cff224e17e3d35e8

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T13:40:53.271150Z digest=sha256:fc6780923c61ff5fb448a1a43b62e06667024bbc88760400489743f2682fc222

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T07:29:45.850226Z digest=sha256:857be92513a4cd862c1d4307df5169d2812b3874f16e8fedf9b29ac4ce99991b

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T09:43:45.019151Z digest=sha256:7347fc7e17fcc3d69e09bf659872567fbe27ff0a9a9c62219efd1a6df887bcec

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.

source=arxiv_source observed=2026-07-11T19:51:56.544026Z digest=sha256:8fb0f9334252cfe395e10c52b1eddf93397a6a8df7aa5ec3acd243658c2d5749