IS05 - Artificial Learning for Marine Engineering: Methods and Applications
Organized by: M. Diez and L. Mainini
The complexity of marine engineering problems (from the analysis of complex physical
phenomena to optimal design and control of marine structures and vessels; from marine
exploration to autonomy and robotics applications) calls for suitable scientific computing
frameworks able to provide cost-effective and reliable solutions. Cutting-edge
methodologies of machine learning (ML) and artificial intelligence (AI) have shown their
potential in providing effective solutions to these problems. However, commonly ML/AI
techniques require significant amounts of data to learn from; in addition, the responses
are often affected by lack of interpretability and their reliability is of difficult
characterization. These features often constitute a limitation to the acceptance of these
techniques for scientific computing in engineering applications since many engineering
problems are associated with high-regret and safety-critical decisions for which the
collection of reference data points is usually expensive. The scientific community is
dedicating efforts to address this limitation, reduce the quantity of data required by the
models, and improve interpretability and reliability of the predictions, therefore paving
the way for a broader adoption and acceptance of ML/AI in engineering applications
[1,2]. The objective of the invited session is to offer a place for discussion on capabilities,
challenges, and open issues for the application of ML/AI to marine engineering. We invite
contributions on different approaches and applications of ML/AI in marine engineering
with the aim of achieving cross-fertilisation of approaches and new ideas.