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About this collection

Extent

1 digital object.

Cite This Work

Hao, Xuanting (2025). Data from: Inferring free surface disturbance properties from Kelvin wakes using convolutional neural network. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0GH9JCX

Description

This collection contains the surface elevation data used for training the convolutional neural networks. The raw data includes 787 simulation cases of Kelvin wakes produced from phase-resolved wave simulations using the high-order spectral method. Affine transformations and random noise have been applied to the raw data to produce this collection. Also included in this collection are images showing the raw surface elevation obtained from wave simulation.

Creation Date

• 2023

Date Issued

• 2025

Author

• Hao, Xuanting

Funding

This work was supported by Naval Innovation, Science, and Engineering Center under ONR grant N000142312831.

Topics

• Algorithm: Supervised learning
• Engineering
• Fluid mechanics and thermal engineering
• Kelvin wake
• Machine learning
• Surface gravity waves
• Task: Regression

Formats

View formats within this collection

• data
• image
• text

Language

• English

Identifier

Identifier: Xuanting Hao: https://orcid.org/0000-0003-4898-1074

Related Resources

Primary associated publication

• Hao, X., 2025, "Inferring free surface disturbance properties from Kelvin wakes using convolutional neural network " Flow. (Submitted).

Collection image

• Image credit: Sai Pramod Anumula. "Kelvin wakes produced by the ASV C-Worker 5 ship."