The design and engineering of ships and platforms that operate in the ocean environment requires understanding of a nonlinear dynamical system that responds according to complex interaction with a wide range of sea and wind conditions. Time domain observation of nonlinear marine dynamics with either experiments or high-fidelity numerical simulation tools is costly due to the random nature of the ocean and the full range of environmental and loading conditions that are experienced in the lifetime of a ship or platform. In this paper, a data-driven method is presented to predict the complex nonlinear input–output relationship typical of marine systems. A Long Short-Term Memory neural net is used to learn nonlinear wave propagation and the nonlinear roll of a ship section in beam seas. Training data are generated with second-order wave theory or a volume-of-fluid computational fluid dynamics, although the method is directly applicable to data that is generated by other means such as nonlinear potential flow or experimental measurements. The cost and the amount of data to apply the method are estimated and measured. The data-driven results are compared with unseen data to demonstrate the accuracy and feasibility.

在海洋环境中运行的船舶和平台的设计和工程需要了解非线性动力系统，该系统根据与各种海洋和风力条件的复杂相互作用做出响应。由于海洋的随机性以及船舶或平台生命周期中所经历的各种环境和载荷条件，使用实验或高保真数值模拟工具对非线性海洋动力学进行时域观测的成本很高。在本文中，提出了一种数据驱动的方法来预测海洋系统典型的复杂非线性输入-输出关系。长短期记忆神经网络用于学习非线性波传播和波束海中船舶部分的非线性横摇。训练数据是使用二阶波理论或流体体积计算流体动力学生成的，尽管该方法直接适用于通过其他方式（如非线性势流或实验测量）生成的数据。估计和测量应用该方法的成本和数据量。将数据驱动的结果与看不见的数据进行比较，以证明其准确性和可行性。