  Designing and modifying complex hull forms for optimal vessel performances
have been a major challenge for naval architects. In the present study,
Principal Component Analysis (PCA) is introduced to compress the geometric
representation of a group of existing vessels, and the resulting principal
scores are manipulated to generate a large number of derived hull forms, which
are evaluated computationally for their calm-water performances. The results
are subsequently used to train a Deep Neural Network (DNN) to accurately
establish the relation between different hull forms and their associated
performances. Then, based on the fast, parallel DNN-based hull-form evaluation,
the large-scale search for optimal hull forms is performed.
