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.