Machine learning and modeling for ship design

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Kaklis, Panagiotis D. and Kostas, Konstantinos and Khan, Shahroz (2025) Machine learning and modeling for ship design. Journal of Marine Science and Engineering, 13 (12). 2304. ISSN 2077-1312 (https://doi.org/10.3390/jmse13122304)

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Abstract

Machine Learning (ML) is a sub-field of Artificial Intelligence (AI), devoted to understanding and building methods that leverage data to improve performance on sets of tasks. Over the last decade, as a result of installing geospatial data systems, measuring and monitoring onboard ships, and proliferation of simulation and optimization algorithms, Big Data has become an established technology in shipping, providing a steadily expanding data flow to industry and research. As a result, the literature distribution of ML applications in shipping has undergone an exponential growth since 2005, reaching thousands of citations per year. One of the first attempts to review the relevant literature in this exponentially growing field was conducted by Huang et al. [1]. The authors highlighted the potential of ML to enhance shipping through various applications while underscoring the need to understand its current limitations and to ensure its reliability by integration with physical methods.

ORCID iDs

Kaklis, Panagiotis D. ORCID logoORCID: https://orcid.org/0000-0002-1843-8815, Kostas, Konstantinos and Khan, Shahroz ORCID logoORCID: https://orcid.org/0000-0003-0298-9089;
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