Paper Citation Record · LEDGER

Benchmarking machine learning models for predicting aerofoil performance

As of 23 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2504.15993.

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

pith.paper-citation-record.v1
2504.15993 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:16:41.279578Z

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One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 048f7975-7c78-4f42-9f4c-02a42625031f · outbound

This paper cites A review of the uk and british channel islands practical tidal stream energy re- source,.

Benchmarking machine learning models for predicting aerofoil performance A review of the uk and british channel islands practical tidal stream energy re- source,

Reference 1

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Source-reported events for the cited work

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Observation 3f7a93a5-846b-4f49-8d2b-ff8ffa32bb7c · outbound

This paper cites Contracts for difference (cfd) allocation round 6: results,.

Benchmarking machine learning models for predicting aerofoil performance Contracts for difference (cfd) allocation round 6: results,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:42.045358Z

Source-reported events for the cited work

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Observation 92266408-d49c-4742-8d12-bdcf75adbe55 · outbound

This paper cites Development and assessment of a blade element momentum theory model for high solidity vertical axis tidal turbines,.

Benchmarking machine learning models for predicting aerofoil performance Development and assessment of a blade element momentum theory model for high solidity vertical axis tidal turbines,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:42.029286Z

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Observation d7b999bf-445b-4bbc-b609-6624c1f36d31 · outbound

This paper cites Rotor blade performance analysis with blade element momentum theory,.

Benchmarking machine learning models for predicting aerofoil performance Rotor blade performance analysis with blade element momentum theory,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:42.013697Z

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Observation 48ba0de6-2225-42a7-857b-5f855ff77552 · outbound

This paper cites Xfoil vs cfd performance predictions for high lift low reynolds number airfoils,.

Benchmarking machine learning models for predicting aerofoil performance Xfoil vs cfd performance predictions for high lift low reynolds number airfoils,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.995670Z

Source-reported events for the cited work

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Observation 2e5119e2-a290-46d4-b3e6-d8954bc861c4 · outbound

This paper cites Cfdbench: A large-scale benchmark for machine learning methods in fluid dynamics,.

Benchmarking machine learning models for predicting aerofoil performance Cfdbench: A large-scale benchmark for machine learning methods in fluid dynamics,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.979648Z

Source-reported events for the cited work

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Observation a36dc52e-ea1c-4d0f-9dff-eb4de1dd54c8 · outbound

This paper cites Megaflow2d: A parametric dataset for machine learning super-resolution in computational fluid dynamics simulations,.

Benchmarking machine learning models for predicting aerofoil performance Megaflow2d: A parametric dataset for machine learning super-resolution in computational fluid dynamics simulations,

Reference 7

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Observation c62ff2bb-c78d-45e3-a813-f75373cf7182 · outbound

This paper cites Airfoil computational fluid dynamics - 2k shapes, 25 aoa’s, 3 re numbers,.

Benchmarking machine learning models for predicting aerofoil performance Airfoil computational fluid dynamics - 2k shapes, 25 aoa’s, 3 re numbers,

Reference 8

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Observation 2c879f0b-aace-4689-bf9d-1f4f676a3af2 · outbound

This paper cites AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions.

Benchmarking machine learning models for predicting aerofoil performance AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions

Reference 9

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Observation 4fedfdca-4bda-4eb2-8194-88d1bac2ea91 · outbound

This paper cites Multilayer feedforward networks are universal approximators,.

Benchmarking machine learning models for predicting aerofoil performance Multilayer feedforward networks are universal approximators,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.946187Z

Source-reported events for the cited work

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Observation 8539c817-a027-4117-94f9-11ca02f0a736 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Benchmarking machine learning models for predicting aerofoil performance Adam: A Method for Stochastic Optimization

Reference 11

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Source-reported events for the cited work

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Observation ac2de8ee-352d-4001-bf4c-72a1f3d86e89 · outbound

This paper cites Prediction of swirling flow field in combustor based on deep learning,.

Benchmarking machine learning models for predicting aerofoil performance Prediction of swirling flow field in combustor based on deep learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.928753Z

Source-reported events for the cited work

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Observation 8d716a60-aed3-4032-a98a-9b66fa617d8a · outbound

This paper cites Artificial neural networks in renewable energy systems applications: a review,.

Benchmarking machine learning models for predicting aerofoil performance Artificial neural networks in renewable energy systems applications: a review,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.912665Z

Source-reported events for the cited work

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Observation fb0d4708-a3e6-42c6-9eb3-db4424014dee · outbound

This paper cites A new model for learning in graph domains,.

Benchmarking machine learning models for predicting aerofoil performance A new model for learning in graph domains,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.897750Z

Source-reported events for the cited work

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Observation 92ea060b-cf3c-4135-a231-2dddbbf4d830 · outbound

This paper cites A comprehensive survey on graph neural networks,.

Benchmarking machine learning models for predicting aerofoil performance A comprehensive survey on graph neural networks,

Reference 15

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Source-reported events for the cited work

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Observation ce4d4844-ac1d-4e2e-a04c-ab0b445d83cb · outbound

This paper cites End-to-end wind turbine wake modelling with deep graph representation learning,.

Benchmarking machine learning models for predicting aerofoil performance End-to-end wind turbine wake modelling with deep graph representation learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.867136Z

Source-reported events for the cited work

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Observation 66f821cf-4ef1-461b-bf59-ffe96413bad0 · outbound

This paper cites Grid adaptive reduced-order model of fluid flow based on graph convolutional neural network,.

Benchmarking machine learning models for predicting aerofoil performance Grid adaptive reduced-order model of fluid flow based on graph convolutional neural network,

Reference 17

Resolution: verified exact; doi, observed 2026-08-16T11:16:41.338208Z

Source-reported events for the cited work

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Observation a8528fb2-acff-49a5-8088-dca291d106b1 · outbound

This paper cites Graph convolutional networks applied to unstructured flow field data,.

Benchmarking machine learning models for predicting aerofoil performance Graph convolutional networks applied to unstructured flow field data,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.850964Z

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Observation add84bfc-ec47-42f6-af24-9140bbdc357c · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Benchmarking machine learning models for predicting aerofoil performance Semi-Supervised Classification with Graph Convolutional Networks

Reference 19

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Observation db8c086c-1439-4c8a-8ebd-cf9ce2ca1f9d · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation,.

Benchmarking machine learning models for predicting aerofoil performance Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.833385Z

Source-reported events for the cited work

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Observation c2453471-9316-44a8-b0bd-d05328051fb1 · outbound

This paper cites Inductive Representation Learning on Large Graphs.

Benchmarking machine learning models for predicting aerofoil performance Inductive Representation Learning on Large Graphs

Reference 21

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Observation 7c67b108-aae1-47f9-9fa7-e15a34baac3c · outbound

This paper cites Graph u-nets,.

Benchmarking machine learning models for predicting aerofoil performance Graph u-nets,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 407e7a27-eb2b-4676-b87e-b1c0f9667917 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Benchmarking machine learning models for predicting aerofoil performance U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 23

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Observation 7ce5acf2-d119-4bfc-886c-452dc5a51beb · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Benchmarking machine learning models for predicting aerofoil performance Fast Graph Representation Learning with PyTorch Geometric

Reference 24

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Source-reported events for the cited work

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Observation ed201fc7-4651-4abc-a873-ab250fdec3c2 · outbound

This paper cites Scalable gradi- ent–enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes,.

Benchmarking machine learning models for predicting aerofoil performance Scalable gradi- ent–enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.803675Z

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Observation 891829a8-0964-4738-a442-a8a62c5bb38a · outbound

This paper cites Introduction to hdf5,.

Benchmarking machine learning models for predicting aerofoil performance Introduction to hdf5,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.782637Z

Source-reported events for the cited work

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Observation 41621655-5d6d-4856-a4fd-1d82523c32f8 · outbound

This paper cites Shapely,.

Benchmarking machine learning models for predicting aerofoil performance Shapely,

Reference 27

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Source-reported events for the cited work

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Observation 8e12b53c-efa0-4c98-9f9e-3c3e86517bf3 · outbound

This paper cites Osher and R.

Benchmarking machine learning models for predicting aerofoil performance Osher and R

Reference 28

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Source-reported events for the cited work

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Observation 19becb4e-97a4-4f13-98fd-d933c5306552 · outbound

This paper cites Intermediate fluid mechanics,.

Benchmarking machine learning models for predicting aerofoil performance Intermediate fluid mechanics,

Reference 29

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Source-reported events for the cited work

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Observation ad16d29f-ad28-4f66-a8d8-3b19c686bb72 · outbound

This paper cites Geometrical effects on the airfoil flow separation and transition,.

Benchmarking machine learning models for predicting aerofoil performance Geometrical effects on the airfoil flow separation and transition,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.709523Z

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Observation 79321d12-b687-46a0-b115-058703707b65 · outbound

This paper cites Effects of relative thickness on aerodynamic characteristics of airfoil at a low reynolds number,.

Benchmarking machine learning models for predicting aerofoil performance Effects of relative thickness on aerodynamic characteristics of airfoil at a low reynolds number,

Reference 31

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Observation 162e9f6d-5587-4596-a0c1-a423cb83ab14 · outbound

This paper cites On the role and chal- lenges of cfd in the aerospace industry,.

Benchmarking machine learning models for predicting aerofoil performance On the role and chal- lenges of cfd in the aerospace industry,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:16:41.675950Z

Source-reported events for the cited work

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Observation 4fc0f91e-72e9-4611-a7a7-9964045aad39 · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

Benchmarking machine learning models for predicting aerofoil performance PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T11:16:41.206621Z

Source-reported events for the cited work

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Observation 27e74597-f504-44cf-a15c-8987d9a2e998 · outbound

This paper cites CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics.

Benchmarking machine learning models for predicting aerofoil performance CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T11:16:41.133296Z

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Pith citing papers

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