Artificial intelligence (AI) is increasingly being used to support modeling and decision-making in ocean engineering. This review, prepared under the auspices of the 31st International Towing Tank Conference (ITTC) Ocean Engineering Committee, examines recent developments and applications of AI across key areas including prediction ocean environment characteristics; loads on ships and offshore structures; AI in station keeping; and AI in ocean renewable energy systems. The review synthesizes findings from approximately 286 publications published from 2020 through March 2026, providing a comprehensive overview of the rapid advances in AI-enabled ocean engineering research. Among the various approaches within AI, machine learning (ML) represents a major class of data-driven methods that learn patterns from data and are widely applied in these areas. Commonly used ML approaches, including neural network-based models, reinforcement learning, and physics-informed methods, are discussed in terms of their applications, data sources, strengths, and limitations. Across different domains, AI has shown strong potential in improving prediction accuracy, supporting control systems, and enabling faster surrogate modeling of complex physical processes. At the same time, challenges remain, particularly in handling complicated models, extreme events, ensuring physical consistency, and transferring models from simulation environments to real operational conditions. Emerging trends such as hybrid physics-AI approaches and real-time integration within digital systems are also highlighted. Overall, this review provides a structured overview of how AI is being applied in ocean engineering and outlines key directions for future development toward more reliable and practical use in real-world applications.

Many ocean engineering problems involve bound harmonics which are slaved to some underlying assumed close to linear time series. When analyzing signals we often want to remove the bound harmonics so as to "linearise" the data or to extract individual bound harmonic components so that they may be studied. For even moderately broadbanded systems filtering in the frequency domain is not sufficient to separate components as they overlap in frequency. One way to overcome this difficulty is to use input signals with the same linear envelope but with different phases and then use simple addition and subtraction of the resulting signals to extract different harmonics. This approach has been established for the analysis of wave groups. In this paper we examine whether this approach can be used on random time series as well. We analyse random wave time series of wave elevation from the towing tank in Shanghai Jiao Tong University and force measurements on a cylinder taken in the Kelvin tank at the University of Strathclyde.