[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120558-en":3,"doc-seo-120558-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120558,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning and Modeling for Ship Design - Editorial","Machine Learning (ML) is reviewed in the context of ship design through the perspective of a Special Issue of the Journal of Marine Science and Engineering. The editorial explains how expanding geospatial data systems, onboard monitoring, and simulation/optimization have accelerated ML adoption since 2005. It summarizes the Special Issue’s goal: advancing ML technologies toward deeper AI embedding while integrating physical reliability. It outlines selected research directions spanning design/analysis, dimensionality reduction and sensitivity, and operational modeling, control, and autonomous systems, including outcomes from the review and selection process.","Editorial  \nMachine Learning and Modeling for Ship Design  \nPanagiotis D. Kaklis 1,2,3,*, Konstantinos Kostas 4,5 and Shahroz Khan 1  \nReceived: 19 November 2025  \nAccepted: 21 November 2025  \nPublished: 4 December 2025  \nCitation: Kaklis, P.D.; Kostas, K.; Khan, S. Machine Learning and Modeling for Ship Design. J. Mar. Sci. Eng. 2025, 13, 2304. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/jmse13122304](10.3390/jmse13122304)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK  \n2 Institute Applied & Computational Mathematics, FORTH (Foundation for Research and Technology—Hellas), 70013 Crete, Greece  \n3 Archimedes Unit, Athena Research Center, 15125 Athens, Greece  \n4 School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan; [konstantinos.kostas@nu.edu.kz](konstantinos.kostas@nu.edu.kz)  \n5 Department of Naval Architecture, University of West Attica, 12241 Athens, Greece  \n* [Correspondence: panagiotis.kaklis@strath.ac.uk](Correspondence: panagiotis.kaklis@strath.ac.uk)  \nMachine 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.  \nThe aim of this Special Issue (SI) was to profile the current status of research versus the next major aim of ML-based research in shipping, namely the need for a deeper embedding in AI of ML technologies. Hence, although the SI was focused on ship design, contributions addressing the aspect of the ship’s operational lifecycle were welcome. In this context, we invited contributions in the following areas:  \n• ML for design and analysis, including supervised/unsupervised techniques and physics-informed models;  \n• Dimensionality reduction in homogeneous or heterogeneous design spaces, latent spaces ship design, and sensitivity analysis in optimization;  \n• ML for operational modeling, control and autonomous systems, among others.  \nThe call for papers for this Special Issue was announced in January 2023 and closed after approximately one year. A large number of submissions were received during that time, with 17 articles (16 research articles and 1 review article: contribution 4) undergoing a rigorous review process and being selected for inclusion in this SI of the Journal of Marine Science and Engineering (JMSE) . To the best of the Guest Editors’ knowledge, this is the first SI that has successfully collected relevant contributions to the area of ML technologies for ship design and operations, providing the readers of JMSE with versatile USPs (unique selling points) . Following this Special Issue, a number of relevant reviews appeared in the pertinent literature which identified similar gaps, trends, and future research directio","cbCaingkFGoVYJsN","https://ap.wps.com/l/cbCaingkFGoVYJsN","pdf",176826,1,6,"English","en",105,"# Overview\n## Motivation and aim of the Special Issue\n## Invited contribution areas\n## Submission, review, and selection process\n# Research themes in the included articles\n## Ship powering and performance prediction\n## Hull form and hydrofoil design and optimization","[{\"question\":\"What motivated the Special Issue on ML for ship design?\",\"answer\":\"The Special Issue aimed to profile the current status of ML research in shipping and emphasize the next major goal: deeper embedding of ML technologies into AI, while supporting reliability through integration with physical methods.\"},{\"question\":\"When was the call for papers announced and when did it close?\",\"answer\":\"The call for papers was announced in January 2023 and closed after approximately one year.\"},{\"question\":\"What main research categories are covered by the selected articles?\",\"answer\":\"The articles are grouped into five main categories, including ship powering and performance prediction, and hull form and hydrofoil design/optimization, among other ship-design and operational themes.\"}]","Machine Learning and Modeling for Ship Design - Editorial | PDF",1785730638,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-modeling-for-ship-design-editorial","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-modeling-for-ship-design-editorial/120558/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What motivated the Special Issue on ML for ship design?","Question",{"text":75,"@type":76},"The Special Issue aimed to profile the current status of ML research in shipping and emphasize the next major goal: deeper embedding of ML technologies into AI, while supporting reliability through integration with physical methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"When was the call for papers announced and when did it close?",{"text":80,"@type":76},"The call for papers was announced in January 2023 and closed after approximately one year.",{"name":82,"@type":73,"acceptedAnswer":83},"What main research categories are covered by the selected articles?",{"text":84,"@type":76},"The articles are grouped into five main categories, including ship powering and performance prediction, and hull form and hydrofoil design/optimization, among other ship-design and operational themes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]