A multi-fidelity prediction model for vertical bending moment and total longitudinal stress of a ship based on composite neural network

In ship engineering, the prediction of vertical bending moment (VBM) and total longitudinal stress (TLS) during ship navigation is of utmost importance. In this work, we propose a new prediction paradigm, the multi-fidelity regression model based on multi-fidelity data and artificial neural network (MF-ANN). Specifically, an ANN is used to learn the fundamental physical laws from low-fidelity data and construct an initial input-output model. The predicted values of this initial model are of low accuracy, and then the high-fidelity data are utilized to establish a correction model that can correct the low-fidelity prediction values. Hence, the overall accuracy of prediction can be improved significantly. The feasibility of the multi-fidelity regression model is demonstrated by predicting the VBM, and the robustness of the model is evaluated at the same time. The prediction of TLS on the deck indicates that just a small amount of high-fidelity data can make the prediction accuracy reach a high level, which further illustrates the validity of the proposed MF-ANN.