作者：尚晓兵 等
页数：168
出版社：哈尔滨工程大学出版社
出版日期：2024
ISBN：9787566142955
电子书格式：pdf/epub/txt
内容简介
随着高性能计算的飞速发展与模型机理研究的不断深入，计算机仿真模型呈现结构复杂、高维、高度非线性的特点，同时仿真模型的运行时间也大幅增加，对复杂仿真模型的建模与分析提出了新的挑战。本书针对复杂模型计算耗时长、高维非线性等问题，以代理模型为技术途径进行实验设计、灵敏度分析、优化设计等方面的研究，并以船舶操纵性为建模对象进行应用。
本书可作为研究复杂系统建模与仿真的参考用书。
目录
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
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
