We construct a multi-fidelity framework for statistical learning and global optimization that is capable of effectively synthesizing low- and high-fidelity simulations towards identifying optimal SWATH hull shapes with superior seakeeping performance. Specifically, we employ multi-fidelity Gaussian process regression and Bayesian optimization to build probabilistic surrogate models and efficiently explore a 35-dimensional design space to optimize hull shapes that minimize wave induced motions and accelerations and satisfy specific requirements in terms of displacement and metacentric height. Our results demonstrate the superior characteristics of this optimization framework in constructing accurate surrogate models and identifying optimal designs with a significant reduction in the computational effort.

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