Paper Citation Record · LEDGER

An application of machine learning to the motion response prediction of floating assets

As of 8 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.15713.

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

pith.paper-citation-record.v1
2506.15713 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:09:24.314828Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

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

28 of 28 outbound references displayed

verified exact9
verified fuzzy14
unresolved3
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f93ecad1-092a-49b5-89ca-0afce18c8fe5 · outbound

This paper cites Deeplearning,.

An application of machine learning to the motion response prediction of floating assets Deeplearning,

Reference 1

Resolution: verified exact; doi, observed 2026-08-07T12:09:26.427751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c04f444-a3bf-4468-9b26-e87aca73a382 · outbound

This paper cites The quiet revolution of numerical weather prediction,.

An application of machine learning to the motion response prediction of floating assets The quiet revolution of numerical weather prediction,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:09:21.358119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72944db4-5781-4540-b26b-2c79858ccb66 · outbound

This paper cites Machine Learning for Fluid Mechanics,.

An application of machine learning to the motion response prediction of floating assets Machine Learning for Fluid Mechanics,

Reference 3

Resolution: verified exact; doi, observed 2026-08-07T12:09:26.190875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 420eabf3-4a4a-48aa-b87b-f4a94218c054 · outbound

This paper cites Deep Generative Models in Engineering De- sign: A Review,.

An application of machine learning to the motion response prediction of floating assets Deep Generative Models in Engineering De- sign: A Review,

Reference 4

Resolution: verified exact; doi, observed 2026-08-07T12:09:25.912742Z

Source-reported events for the cited work

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Observation 0cb24886-36c5-4ca6-87b0-9ff4f24e40e5 · outbound

This paper cites Neuralgeneralcirculationmodelsforweatherand climate,.

An application of machine learning to the motion response prediction of floating assets Neuralgeneralcirculationmodelsforweatherand climate,

Reference 5

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Source-reported events for the cited work

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Observation 8729f274-32aa-49e6-bc2a-96fc231215e9 · outbound

This paper cites Neural networks in the dy- namicresponseanalysisofslendermarinestructures,.

An application of machine learning to the motion response prediction of floating assets Neural networks in the dy- namicresponseanalysisofslendermarinestructures,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:30.424208Z

Source-reported events for the cited work

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Observation 90646776-f698-4bbb-b792-b20e1c27a37d · outbound

This paper cites ANN- basedsurrogatemodelsfortheanalysisofmooringlinesandrisers,.

An application of machine learning to the motion response prediction of floating assets ANN- basedsurrogatemodelsfortheanalysisofmooringlinesandrisers,

Reference 7

Resolution: verified exact; doi, observed 2026-08-07T12:09:25.689110Z

Source-reported events for the cited work

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Observation 0af2b6ef-8097-4d1c-a86b-c1dc2c39351e · outbound

This paper cites MachineLearningBasedMooredShipMovement Prediction,.

An application of machine learning to the motion response prediction of floating assets MachineLearningBasedMooredShipMovement Prediction,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:30.229639Z

Source-reported events for the cited work

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Observation ad4c7de9-4dc9-4d27-8c03-291e7e38ad77 · outbound

This paper cites Real-time prediction of 6-DOF motions of a turret-moored FPSO in harsh sea state,.

An application of machine learning to the motion response prediction of floating assets Real-time prediction of 6-DOF motions of a turret-moored FPSO in harsh sea state,

Reference 9

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Source-reported events for the cited work

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Observation 3750344e-5ac4-416a-9ec7-590e66ce0f85 · outbound

This paper cites A new approach to predict dynamic mooringtensionusingLSTMneuralnetworkbasedonresponsesoffloatingstructure,.

An application of machine learning to the motion response prediction of floating assets A new approach to predict dynamic mooringtensionusingLSTMneuralnetworkbasedonresponsesoffloatingstructure,

Reference 10

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Source-reported events for the cited work

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Observation f0f7cabb-3f27-421c-9c13-614f3785aecf · outbound

This paper cites Machine-learning-basedvirtualloadsen- sorsformooringlinesusingsimulatedmotionandlidarmeasurements,.

An application of machine learning to the motion response prediction of floating assets Machine-learning-basedvirtualloadsen- sorsformooringlinesusingsimulatedmotionandlidarmeasurements,

Reference 11

Resolution: verified exact; doi, observed 2026-08-07T12:09:25.362273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 09215299-e9d0-4faf-97a4-9047eb2f9ad7 · outbound

This paper cites XGBoost: A Scalable Tree Boosting System,.

An application of machine learning to the motion response prediction of floating assets XGBoost: A Scalable Tree Boosting System,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:30.069555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8d14468a-6af2-4eb4-b305-a9c614648449 · outbound

This paper cites Measurementsofwind-wavegrowthandswell decay during the joint North Sea wave project (JONSWAP).,.

An application of machine learning to the motion response prediction of floating assets Measurementsofwind-wavegrowthandswell decay during the joint North Sea wave project (JONSWAP).,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:29.811824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58987c2b-4055-46fe-a26b-cc4e007068f6 · outbound

This paper cites JONSWAP’sparameters:Sortingouttheinconsistencies,.

An application of machine learning to the motion response prediction of floating assets JONSWAP’sparameters:Sortingouttheinconsistencies,

Reference 14

Resolution: verified exact; doi, observed 2026-08-07T12:09:25.133108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 27463771-b62a-410d-b000-ee815553f79b · outbound

This paper cites Recommended practice: Environmental conditions and environmental loads,.

An application of machine learning to the motion response prediction of floating assets Recommended practice: Environmental conditions and environmental loads,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:29.612692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7d405023-5cc6-4519-8c1a-47b41d99b93e · outbound

This paper cites PetroleumandNaturalGasIndustries—SpecificRequirementsforOffshoreStructures —Part1:Metoceandesignandoperatingconsiderations,.

An application of machine learning to the motion response prediction of floating assets PetroleumandNaturalGasIndustries—SpecificRequirementsforOffshoreStructures —Part1:Metoceandesignandoperatingconsiderations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:29.370043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2a37f955-bfb4-424f-9b1b-7a522b27d5b9 · outbound

This paper cites [Online].

An application of machine learning to the motion response prediction of floating assets [Online]

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:29.129025Z

Source-reported events for the cited work

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Observation 673ec042-67c1-4ccc-b360-93c47a3d17fa · outbound

This paper cites orcina.com/webhelp/OrcaWave/.

An application of machine learning to the motion response prediction of floating assets orcina.com/webhelp/OrcaWave/

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:28.821078Z

Source-reported events for the cited work

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Observation d883a6cd-551e-42b9-9dd3-c6f5e37dd78d · outbound

This paper cites Secondorder,slowlyvaryingforcesonvesselsinirregularwaves,.

An application of machine learning to the motion response prediction of floating assets Secondorder,slowlyvaryingforcesonvesselsinirregularwaves,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:28.528112Z

Source-reported events for the cited work

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Observation 869b6470-a7d4-48dd-a8d7-e2c12a63e846 · outbound

This paper cites Aformulafor‘wavedamping’inthedriftofafloatingbody,.

An application of machine learning to the motion response prediction of floating assets Aformulafor‘wavedamping’inthedriftofafloatingbody,

Reference 20

Resolution: verified exact; doi, observed 2026-08-07T12:09:24.896643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 285fd9fa-2186-463a-b9e6-db64c69a4bb9 · outbound

This paper cites Recommended Procedures and Guidelines: Estimation of Roll Damping,.

An application of machine learning to the motion response prediction of floating assets Recommended Procedures and Guidelines: Estimation of Roll Damping,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:28.270354Z

Source-reported events for the cited work

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Observation 9fa461ee-76b0-446d-aa39-cd34623605a0 · outbound

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An application of machine learning to the motion response prediction of floating assets Unresolved cited work

Reference 22

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Source-reported events for the cited work

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Observation b096fbc0-b832-48a8-9d84-734c9311bdaa · outbound

This paper cites Random Forests,.

An application of machine learning to the motion response prediction of floating assets Random Forests,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:27.738676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e0473f4d-ca39-4584-af13-ea235cd5d15a · outbound

This paper cites Scikit-learn: Machine Learning in Python,.

An application of machine learning to the motion response prediction of floating assets Scikit-learn: Machine Learning in Python,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:27.425382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 82db07d5-2bf3-4ee7-b327-ba4320aed368 · outbound

This paper cites LightGBM: A Highly Efficient Gradient Boosting Decision Tree,.

An application of machine learning to the motion response prediction of floating assets LightGBM: A Highly Efficient Gradient Boosting Decision Tree,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:27.192915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5b6eed4c-9383-414a-8a83-fba08b68ac22 · outbound

This paper cites Random Search for Hyper-Parameter Optimization,.

An application of machine learning to the motion response prediction of floating assets Random Search for Hyper-Parameter Optimization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:09:26.974178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f9b0a098-c70b-4be1-aab6-b74adbc7a49c · outbound

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An application of machine learning to the motion response prediction of floating assets Unresolved cited work

Reference 565

Resolution: verified exact; doi, observed 2026-08-07T12:09:24.580324Z

Source-reported events for the cited work

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Observation 41a4e231-d0fe-49af-92a5-6123bb4f0baf · outbound

This paper cites an unresolved cited work.

An application of machine learning to the motion response prediction of floating assets Unresolved cited work

Reference 1312

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.