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Special Issue "Machine Learning and Modeling for Ship Design"
A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".
Deadline for manuscript submissions: 20 June 2023 | Viewed by 1689
Special Issue Editors
Interests: isogeometric analysis (IGA); naval hydrodynamics; computer-aided geometric design; CAD; parametric modelling; shape optimisation; dimensionality reduction; virtual environments
Special Issues, Collections and Topics in MDPI journals
Interests: virtual environments with applications in naval architecture & marine engineering; parametric geometrical modeling & design optimization; application of the isogeometric concept in engineering & ship design
Interests: computer-aided design & engineering; machine learning; generative design; shape optimisation; design intelligence
Special Issue Information
Machine Learning (ML) is a sub-field of Artificial Intelligence (AI), devoted to understanding and building methods that leverage data to improve performance on some set of tasks. A 2020 Deloitte survey found that 67% of companies are using machine learning, and 97% are using or planning to use it in the next year. During the last decade, as a result of installing geospatial data systems, measuring and monitoring onboard ships, and proliferation of using simulation and optimization algorithms, Big Data has been established in Shipping providing a steadily expanding data flow to industry and research. As a result, the literature distribution of ML applications in Shipping has experienced an exponential growth since 2005, having reached thousands of citations per year.
The aim of this Special Issue (SI) is 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 such as Neural Networks (NNs), recurrent NNs (RNNs), autoencoders, support vector machines (SVMs), convolutional NNs (CNNs), generative adversarial networks (GANs), etc. Since it is generally accepted that around 70% of the manufacturing costs of a product can be derived from design decisions, our SI will focus on Ship Design including its impact on lifecycle operation issues.
More specifically, we are looking for papers dealing with one or more themes from the following, not exhaustive, list:
- ML for design and analysis: estimation of main particulars and conceptual design, fluid-flow modeling and resistance predictions, turbulence modeling, wind/wave induced loads modeling, hull/propeller/foils design and modelling, etc.
- ML, dimensionality reduction (DR) and sensitivity analysis (SA) in Optimization: versatility and capacity of design spaces, intrasensitivity, ship and/or systems design optimization, shipbuilding optimization, design for reliability, etc.
- ML for operational modeling: wind and/or wave forecasting and ship loading, route design and prediction, systems condition monitoring, predictive maintenance, fuel consumption and engine power predictions, stability, maneuvering, docking / collision avoidance, JIT (just-in-time) arrival, human-factor modeling for accidents prevention, etc.
- ML for autonomous systems: autonomous ships, autonomous vehicles for inspection, design for autonomous maintenance operations, rerouting and automatic docking/ maneuvering, etc.
- Mixed-initiative generative learning models: intelligent learning systems combining artificial and human agents to work corporately and complementarily during the training process.
We especially welcome works contributing to cutting-edge topics, such as:
- Physics-informed ML tools;
- Moment-driven ML tools for DR/SA;
- Unsupervised Learning in heterogeneous Design Spaces.
Prof. Dr. Panagiotis D. Kaklis
Dr. Konstantinos Kostas
Dr. Shahroz Khan
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- artificial intelligence
- machine learning
- ship design
- parametric modeling
- design spaces
- mixed-initiative modelling
- shape optimization
- dimensionality reduction
- sensitivity analysis
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Parameterized Dataset of Ship Hulls with Geometric and Performance Characteristics
Authors: Noah Bagazinski (email@example.com) & Faez Ahmed (firstname.lastname@example.org)
Affiliation: Department of Mechanical Engineering, Massachusetts Institute of Technology