标题： 基于主成分分析和深度神经网络的船体线型优化 显示英文标题

标题： Hull Form Optimization with Principal Component Analysis and Deep Neural Network

摘要： 为实现船舶最佳性能而设计和修改复杂船体形式一直是船舶工程师面临的主要挑战。 在本研究中，引入了主成分分析（PCA）来压缩一组现有船舶的几何表示，随后对得到的主成分得分进行操作，生成大量派生船体形式，并对其在平静水域中的性能进行计算评估。 随后，利用这些结果训练深度神经网络（DNN），以准确建立不同船体形式与其相关性能之间的关系。 然后，基于快速、并行的基于DNN的船体评估，进行大规模的最优船体形式搜索。 显示英文摘要

摘要： 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.