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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Application of Machine Learning to Early-Stage Hull Form Design
Austin Kyle Shaeffer;
Paper presented at the SNAME Maritime Convention, Virtual, September 2020.
Paper Number: SNAME-SMC-2020-098
Published: September 29 2020
Shaeffer, Austin Kyle, Wilson, Wesley, and Chi Yang. "Application of Machine Learning to Early-Stage Hull Form Design." Paper presented at the SNAME Maritime Convention, Virtual, September 2020.
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