Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning
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DOI: 10.1016/j.apenergy.2022.119711
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Cited by:
- Pang, Junheng & Dong, Sheng, 2023. "A novel multivariable hybrid model to improve short and long-term significant wave height prediction," Applied Energy, Elsevier, vol. 351(C).
- Zhang, Jincheng & Zhao, Xiaowei & Greaves, Deborah & Jin, Siya, 2023. "Modeling of a hinged-raft wave energy converter via deep operator learning and wave tank experiments," Applied Energy, Elsevier, vol. 341(C).
- Li, Rui & Zhang, Jincheng & Zhao, Xiaowei & Wang, Daming & Hann, Martyn & Greaves, Deborah, 2023. "Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments," Applied Energy, Elsevier, vol. 348(C).
- Wu, Han & Liang, Yan & Gao, Xiao-Zhi, 2023. "Left-right brain interaction inspired bionic deep network for forecasting significant wave height," Energy, Elsevier, vol. 278(PB).
- Liu, Yue & Zhang, Xiantao & Dong, Qing & Chen, Gang & Li, Xin, 2024. "Phase-resolved wave prediction with linear wave theory and physics-informed neural networks," Applied Energy, Elsevier, vol. 355(C).
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Keywords
Bayesian neural network; Deterministic sea wave prediction; Predictable zone; Probabilistic machine learning; Uncertainty quantification; Wave tank experiment;All these keywords.
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