Leveraging a Small Dataset to Predict Nonlinear Global Loads in Irregular Waves

Abstract

In this work, a hybrid machine learning method, which uses ML strategies to model high-order force components within a low-order equation of motion, is considered in the context of the global wave-induced loads of a ship in irregular waves. It is shown that the method can make predictions in a range of wave conditions even when the training data set only includes a single seaway. The proposed method offers a data-leveraging technique which may be useful in the design space, where a small data set derived from a high-fidelity source can be leveraged to make similar fidelity predictions in a larger number of wave conditions.