A multi-fidelity prediction model for vertical bending moment and total longitudinal stress of a ship based on composite neural network
作者机构：Science and Technology on Advanced Composites in Special Environments Key LaboratoryHarbin Institute of TechnologyHarbin150001China China Ship Scientific Research CenterWuxi214082China School of Aeronautics and AstronauticsZhejiang UniversityHangzhou310027China College of Control Science and EngineeringZhejiang UniversityHangzhou310027China
出 版 物：《Journal of Hydrodynamics》 (水动力学研究与进展B辑（英文版）)
年 卷 期：2023年第35卷第1期
页 面：27-35页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Key Research amd Development Program of China(Grant No.2020YFA0405700)
主 题：Artificial neural network multi-fidelity vertical bending moment total longitudinal stress
摘 要：In ship engineering,the prediction of vertical bending moment(VBM)and total longitudinal stress(TLS)during ship navigation is of utmost *** 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 *** 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 ***,the overall accuracy of prediction can be improved *** feasibility of the multi-fidelity regression model is demonstrated by predicting the VBM,and the robustness of the model is evaluated at the same *** 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.
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