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

[1]  Yuanchang Liu,et al.  Machine learning in sustainable ship design and operation: A review , 2022, Ocean Engineering.

[2]  Zhiyong Pei,et al.  Stress-distribution characteristics of cruise ship based on multiple-beam method , 2022, Ocean Engineering.

[3]  Z. Zong,et al.  A numerical study of hydrodynamic influence on collision of brash ice with a structural plate , 2022, Journal of Hydrodynamics.

[4]  D. Cebrián.  An Introduction to Artificial Neural Networks , 2021, Proceedings of MOL2NET'21, Conference on Molecular, Biomedical & Computational Sciences and Engineering, 7th ed..

[5]  Long Tran-Thanh,et al.  A machine learning approach for monitoring ship safety in extreme weather events , 2021 .

[6]  Young-Rong Kim,et al.  Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship In-Service Data , 2021, Journal of Marine Science and Engineering.

[7]  Barween Al Kurdi,et al.  IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare - A Review , 2021, Future Internet.

[8]  Peng Fang,et al.  Vehicle automatic driving system based on embedded and machine learning , 2020, 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL).

[9]  Kishor P. Upla,et al.  Smart Home Automation Using Machine Learning Algorithms , 2020, 2020 International Conference for Emerging Technology (INCET).

[10]  2020 International Conference for Emerging Technology (INCET) , 2020 .

[11]  George Em Karniadakis,et al.  A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems , 2019, J. Comput. Phys..

[12]  K. Iijima,et al.  Efficient FORM-based extreme value prediction of nonlinear ship loads with an application of reduced-order model for coupled CFD and FEA , 2019, Journal of Marine Science and Technology.

[13]  P. Temarel,et al.  Fully coupled CFD/FEA investigations to predict the wave loads on a flexible containership , 2018 .

[14]  Kazuhiro Iijima,et al.  Numerical Investigation Into Uncertainty of Wave-Induced Vibration of Large Container Ships due to Ship Operation , 2017 .

[15]  Lei Shi,et al.  Development of ship structure health monitoring system based on IOT technology , 2017 .

[16]  Paris Perdikaris,et al.  Inferring solutions of differential equations using noisy multi-fidelity data , 2016, J. Comput. Phys..

[17]  R. Haftka,et al.  Review of multi-fidelity models , 2016, Advances in Computational Science and Engineering.

[18]  Eliopoulou Eleftheria,et al.  Statistical Analysis of Ship Accidents and Review of Safety Level , 2016 .

[19]  Tie-Yan Liu,et al.  On the Depth of Deep Neural Networks: A Theoretical View , 2015, AAAI.

[20]  Jinxian Weng,et al.  Investigation of shipping accident injury severity and mortality. , 2015, Accident; analysis and prevention.

[21]  Huynh Van-Vu,et al.  Prediction the Ultimate Longitudinal Strength of Intact Ship by Finite Element Method , 2015 .

[22]  Nan Zhao,et al.  Wave Loads Investigation of a VLCC by Experimental and Theoretical Methods , 2014 .

[23]  Vedran Žanić,et al.  Methods and concepts for the multi-criteria synthesis of ship structures , 2013 .

[24]  Alexander I. J. Forrester,et al.  Multi-fidelity optimization via surrogate modelling , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  C. Soares,et al.  Experimental investigation of the nonlinear effects on the vertical motions and loads of a containership in regular waves , 2004 .

[26]  Jeom Kee Paik,et al.  Ship structural safety and reliability , 2001 .

[27]  Xiang-Sun Zhang,et al.  Introduction to Artificial Neural Network , 2000 .