With increasingly inexpensive computational resources, big data and machine learning are more available and approachable than ever before. Many industries are moving towards including big data analytics in their current processes and having success using machine learning techniques to improve existing systems and methods. The drive within the naval architecture community over the last couple of decades towards set-based design and design space exploration has resulted in increasingly large and more readily obtainable sets of ship design data. This research focuses on coupling the state-of-the-art in machine learning techniques with the increasingly available ship design data in order to improve the hull form design process.

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