...., Refs. [ 9 – 14 ]. This paper focuses on adaptive data-based methods, specifically statistical methods compared to machine learning methods, and is based on a Master’s thesis in Data Science from the University of Oslo [ 15 ]. The study presented in this paper is also intimately related to another...

... 24 06 2024 25 06 2024 03 09 2024 Graphical Abstract Figure ocean current direction short-term forecasting machine learning empirical mode decomposition warped Gaussian process ocean energy technology offshore safety and reliability ocean currents and associated...

... 2024 17 07 2024 22 07 2024 20 08 2024 Graphical Abstract Figure multivariate time series forecasting whipping response machine learning dynamics of structures fluid–structure interaction structural safety and risk analysis National Natural Science...

... make validation difficult. The next best option is that of controlled laboratory experiments. The current study is the first study in the field of sea spray icing that investigates the use of new data science technologies like machine learning and feature engineering for the prediction of sea spray...

..., sets the forcing term of a PDE as f and its solution as g . The reader is referred to Refs. [ 9 , 10 ] for further reading on graph neural networks (GNNs) and on neural operators, use cases, and how they compare to other forms of machine learning. We apply this model to a virtual tank...

... of ASME for publication in the J ournal of O ffshore M echanics and A rctic E ngineering . 22 06 2023 16 10 2023 20 10 2023 11 12 2023 fluid–structure interaction machine learning hydrodynamics Engineering and Physical Sciences Research Council 10.13039...

... examined the generalization of the models across different sea states and investigated the impact of the second-order bound waves on prediction accuracy. This paper is organized as follows. Section 2 introduces linear and second-order nonlinear waves. Section 3 describes the proposed machine learning...

... be complemented with buoy measurements. This paper proposes to use synthetic statistics of vessel dynamic responses to predict “real-time” wave spectral parameters using multi-output machine learning (ML) regression algorithms. For a case study of a vessel with no forward speed, it is observed that the random...

...Susmita Saha; Soumen De; Satyasaran Changdar The stability analysis of breakwaters is very important to have a safe and economic design of these coastal protective structures and the damage level is one of the most important parameters in this context. In the recent past, machine learning...

...J. P. Panda; H. V. Warrior Computationally efficient and accurate simulations of the flow over axisymmetric bodies of revolution (ABR) have been an important desideratum for engineering design. In this article, the flow field over an ABR is predicted using machine learning (ML) algorithms (e.g...

...Yu Ma; Paul D. Sclavounos Data-driven modeling is considered as a prospective approach for many conventional physical problems including ocean applications. Among various machine learning techniques, support vector machine stands out as one of the most widely used algorithms to establish models...