Dear Colleagues,

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
Guest Editors

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• artificial intelligence
• machine learning
• ship design
• parametric modeling
• design spaces
• mixed-initiative modelling
• shape optimization
• dimensionality reduction
• sensitivity analysis

Title: A Parameterized Dataset of Ship Hulls with Geometric and Performance Characteristics
Authors: Noah Bagazinski (noahbagz@mit.edu) & Faez Ahmed (faez@mit.edu)
Affiliation: Department of Mechanical Engineering, Massachusetts Institute of Technology