Phase-resolved wave prediction with linear wave theory and physics-informed neural networks
Deterministic wave elevation prediction is crucial for improving the power generation
efficiency of offshore energy structures (OESs). Although phase-resolved wave models may …
efficiency of offshore energy structures (OESs). Although phase-resolved wave models may …
An integrated complete ensemble empirical mode decomposition with adaptive noise to optimize LSTM for significant wave height forecasting
L Zhao, Z Li, J Zhang, B Teng - Journal of Marine Science and …, 2023 - mdpi.com
In recent years, wave energy has gained attention for its sustainability and cleanliness. As
one of the most important parameters of wave energy, significant wave height (SWH) is …
one of the most important parameters of wave energy, significant wave height (SWH) is …
Machine learning simulation of one-dimensional deterministic water wave propagation
Deterministic phase-resolved prediction of the evolution of surface gravity waves in water is
challenging due to their complex spatio-temporal dynamics. Physics-based methods of …
challenging due to their complex spatio-temporal dynamics. Physics-based methods of …
[HTML][HTML] Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks
The measurement of deep water gravity wave elevations using in situ devices, such as wave
gauges, typically yields spatially sparse data due to the deployment of a limited number of …
gauges, typically yields spatially sparse data due to the deployment of a limited number of …
Predicting heave and pitch motions of an FPSO using meta-learning
Real-time motion prediction is helpful in guaranteeing the operation stability of a Floating
Production Storage Offloading (FPSO) unit. Recurrent neural networks (RNNs) are …
Production Storage Offloading (FPSO) unit. Recurrent neural networks (RNNs) are …
Enhancing deterministic prediction in unidirectional ocean waves using an Artificial Neural Network with exponential linear unit
Z Feng, Z Wang, K Zheng, R Li, Y Zhao, Y Wang - Ocean Engineering, 2024 - Elsevier
The phase-resolved wave prediction based on physical methods is difficult to ensure both
accuracy and efficiency simultaneously. In recent years, Artificial Neural Network (ANN) has …
accuracy and efficiency simultaneously. In recent years, Artificial Neural Network (ANN) has …
[HTML][HTML] Faster than real-time, phase-resolving, data-driven model of wave propagation and wave–structure interaction
JC Harris - Applied Ocean Research, 2025 - Elsevier
A machine learning time-series prediction approach is proposed for wave propagation and
wave load prediction. Under unidirectional wave conditions and variable bathymetry, given …
wave load prediction. Under unidirectional wave conditions and variable bathymetry, given …
Optimized quiescent period prediction under harsh sea states using a linear wave model based on physics-informed neural networks
Quiescent period prediction (QPP) is a robust assurance for maritime activities' safe and
efficient conduct. This study opts for a physics-driven approach to achieve long-term QPP …
efficient conduct. This study opts for a physics-driven approach to achieve long-term QPP …
Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
The assimilation and prediction of phase-resolved surface gravity waves are critical
challenges in ocean science and engineering. Potential flow theory (PFT) has been widely …
challenges in ocean science and engineering. Potential flow theory (PFT) has been widely …
Data-Driven Generation of Tailored Wave Sequences
This paper explores the applicability of machine learning techniques for the generation of
tailored wave sequences. For this purpose, a fully convolutional neural network was …
tailored wave sequences. For this purpose, a fully convolutional neural network was …