AI-Driven Optimization of Ship Performance and Navigation Safety

Dear Colleagues,

The rapid digitalization of the maritime sector, driven by advances in artificial intelligence (AI), high‑fidelity simulations, sensor technologies, and autonomous systems, is transforming how vessels are designed, operated, and managed. As global shipping continues to face rising demands for efficiency, safety, and sustainability, AI‑driven optimization has emerged as a powerful enabler for addressing complex challenges such as fuel reduction, emissions control, real‑time navigation, and risk‑aware decision support. These technological trends are reshaping the landscape of marine engineering, offering unprecedented opportunities to enhance ship performance while ensuring safer maritime operations.

Furthermore, ships today operate in increasingly dynamic and unpredictable environments, ranging from congested sea lanes to extreme weather conditions influenced by climate change. Traditional analytical or rule‑based methods often fall short in capturing the nonlinear and time‑varying nature of maritime systems. AI‑enabled models, especially those leveraging machine learning, digital twins, reinforcement learning, and big data analytics, provide new pathways for predictive capability, operational optimization, advanced failure detection, and autonomous navigation safety. As the maritime industry accelerates toward smart shipping and autonomous vessels, rigorous scientific research is urgently needed to support trustworthy, interpretable, and robust AI applications for marine systems.

This Special Issue aims to present and disseminate the most recent advances related to AI‑driven optimization of ship performance, operational efficiency, and navigation safety. We welcome high‑quality research contributions that explore innovative AI methodologies, data‑driven modelling, maritime decision support systems, and integrated frameworks bridging hydrodynamics, structural integrity, and navigational risk. Both fundamental research and application‑oriented studies with relevance to real‑world marine engineering practice are encouraged.

Topics of interest for publication include, but are not limited to, the following:

• AI‑based ship performance prediction and optimization
• Machine learning and digital twins for structural integrity and lifecycle assessment
• Intelligent route planning, maneuvering optimization, and weather‑aware navigation
• Reinforcement learning for autonomous ship control and collision avoidance
• Data‑driven risk assessment and maritime safety management
• Predictive maintenance and fault diagnosis for ship machinery and systems
• AI‑enhanced hydrodynamic and sea‑keeping performance modelling
• Multi‑objective optimization for green, efficient, and resilient ship operations
• Sensor fusion, situational awareness, and maritime perception systems
• Human–AI collaboration and decision support on the bridge

Prof. Dr. Wengang Mao
Prof. Dr. Lianzhong Huang
Prof. Dr. Jinlu Sheng
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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 2600 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.

• AI‑driven ship optimization
• machine learning in marine engineering
• navigational safety
• autonomous ships
• digital twins
• voyage optimization
• collision avoidance
• hydrodynamic performance modelling