Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition
Matteo Diez,
Mauro Gaggero,
Andrea Serani
Abstract:SummaryData‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches. Numerical results in two case studies involving the course‐keeping of a naval destroyer in a high sea state using simulation data at model scale are presented. The proposed methods reveal successful in predicting ship motions b… Show more
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