Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning

Jincheng Zhang; Xiaowei Zhao; Siya Jin; Deborah Greaves

doi:10.1016/j.apenergy.2022.119711

Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning

Jincheng Zhang, Xiaowei Zhao^*, Siya Jin, Deborah Greaves

^*Corresponding author for this work

School of Engineering, Computing and Mathematics

University of Warwick

Research output: Contribution to journal › Article › peer-review

10 Downloads (Pure)

Original language	English
Number of pages	0
Journal	Applied Energy
Volume	324
Issue number	0
Early online date	26 Jul 2022
DOIs	https://doi.org/10.1016/j.apenergy.2022.119711
Publication status	Published - 15 Oct 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.apenergy.2022.119711

Manuscript

Cite this

@article{cb4fef77f8ec4171a9d6131c47836905,

title = "Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning",

author = "Jincheng Zhang and Xiaowei Zhao and Siya Jin and Deborah Greaves",

year = "2022",

month = oct,

day = "15",

doi = "10.1016/j.apenergy.2022.119711",

language = "English",

volume = "324",

journal = "Applied Energy",

issn = "0306-2619",

publisher = "Elsevier B.V.",

number = "0",

}

TY - JOUR

T1 - Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning

AU - Zhang, Jincheng

AU - Zhao, Xiaowei

AU - Jin, Siya

AU - Greaves, Deborah

PY - 2022/10/15

Y1 - 2022/10/15

UR - https://pearl.plymouth.ac.uk/context/secam-research/article/1511/viewcontent/Manuscript.pdf

U2 - 10.1016/j.apenergy.2022.119711

DO - 10.1016/j.apenergy.2022.119711

M3 - Article

SN - 0306-2619

VL - 324

JO - Applied Energy

JF - Applied Energy

IS - 0

ER -

Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning

UN SDGs

Access to Document

Other files and links

Cite this