Chapter 1 Introduction 1.1 Surrogate Model 1.2 Design of Experiments 1.3 Global Sensitivity Analysis 1.4 Book Overview Chapter 2 Optimal Latin Hypercube Design Using Local Search-based Genetic Algorithm 2.1 Optimal Latin Hypereube Design 2.2 Local Search-based Genetic Algorithm for LHD Optimization 2.3 Performance Comparison of Optimization Methods 2.4 Summary Chapter 3 Active Learning of Multi-kernel Kriging Surrogate Models Using Regional Discrepancy and Space-ffiling Criteria 3.1 Formulation of Ensemble Surrogate Model 3.2 Ensemble Learning for Kriging Surrogate Models 3.3 Experimental Study 3.4 Summary Chapter 4 Derivative-based Global Sensitivity Measure Using Radial Basis Function 4.1 Estimation of Kernel Width for RBF 4.2 DGSM Estimator Using RBF 4.3 Experimental Study 4.4 Summary Chapter 5 Polynomial Chaos Expansion-enhanced Gaussian Process Regression for Global Sensitivity Analysis 5.1 GPR Surrogate Model 5.2 Global Sensitivity Analysis Using PCEGPR 5.3 Experimental Study 5.4 Summary Chapter 6 Multi-fidelity Kriging Method for Global Sensitivity Analysis 6.1 Cokriging Surrogate Model 6.2 Sobol Indices Based on Cokriging Model 6.3 Experimental Study 6.4 Summary Chapter 7 Reliability-based Design Optimization Using Polynomial Chaos Expansion-enhanced Radial Basis Function Method 7.1 Formulation of RBDO Problem 7.2 Extended Radial Basis Function 7.3 PCE-RBF for RBDO 7.4 Experimental Study 7.5 Summary Chapter 8 Application of Surrogate Model for Ship Maneuvering Motion Modelling 8.1 Formulation of Ship Dynamic Model 8.2 Nonparametric Modelling 8.3 Parametric Identification 8.4 Summary Reference