{"product_id":"surrogate-modeling-and-optimization-hardcover","title":"Surrogate Modeling and Optimization - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eNam-Ho Kim\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eExpert reference on building surrogate models, optimization using them, prediction uncertainty associate with them, and their potential failure, with practical implementation in MATLAB\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eSurrogate Modeling and Optimization\u003c\/i\u003e explains the meaning of different surrogate models and provides an in-depth understanding of such surrogates, emphasizing how much uncertainty is associated with them, and when and how a surrogate model can fail in approximating complex functions and helping readers understand theory through practical implementation in MATLAB. This book enables readers to obtain an accurate approximate function using as few samples as possible, thereby allowing them to replace expensive computer simulations and experiments during design optimization, sensitivity analysis, and\/or uncertainty quantification. \u003c\/p\u003e\u003cp\u003eThe book is organized into three parts. Part I introduces the basics of surrogate modeling. Part II reviews various theories and algorithms of design optimization. Part III presents advanced topics in surrogate modeling, including the Kriging surrogate, neural network models, multi-fidelity surrogates, and efficient global optimization using Kriging surrogates. \u003c\/p\u003e\u003cp\u003eThe book is divided into 10 chapters. Each chapter contains about 10 examples and 20 exercise problems. Lecture slides and a solution manual for exercise problems are available for instructors on a companion website. \u003c\/p\u003e\u003cp\u003eSample topics discussed in \u003ci\u003eSurrogate Modeling and Optimization\u003c\/i\u003e include: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eVarious designs of experiments, such as those developed for linear and quadratic polynomial response surfaces (PRS) in a boxlike design space\u003c\/li\u003e \u003cli\u003eCriteria for constrained and unconstrained optimization and the most important optimization theories\u003c\/li\u003e \u003cli\u003eVarious numerical algorithms for gradient-based optimization\u003c\/li\u003e \u003cli\u003eGradient-free optimization algorithms, often referred to as global search algorithms, which do not require gradient or Hessian information\u003c\/li\u003e \u003cli\u003eDetailed explanations and implementation on Kriging surrogate, often referred to as Gaussian Process, especially when samples include noise\u003c\/li\u003e \u003cli\u003eThe combination of a small number of high-fidelity samples with many low-fidelity samples to improve prediction accuracy\u003c\/li\u003e \u003cli\u003eNeural network models, focusing on training uncertainty and its effect on prediction uncertainty\u003c\/li\u003e \u003cli\u003eEfficient global optimization using either polynomial response surfaces or Kriging surrofates\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eSurrogate Modeling and Optimization\u003c\/i\u003e is an essential learning companion for senior-level undergraduate and graduate students in all engineering disciplines, including mechanical, aerospace, civil, biomedical, and electrical engineering. The book is also valuable for industrial practitioners who apply the surrogate model to solve their optimization problems.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eNam-Ho Kim\u003c\/b\u003e is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Florida. His research interests include design under uncertainty, prognostics and health management, verification validation and uncertainty quantification, and nonlinear structural mechanics. He has more than twenty years of experience teaching materials in these fields to graduate students.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 466\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 30, 2025\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43154178900031,"sku":"9781394245819","price":194.4,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/t114KPmowg9781394245819.webp?v=1776954466","url":"https:\/\/dhl-adrianne.myshopify.com\/products\/surrogate-modeling-and-optimization-hardcover","provider":"BBB","version":"1.0","type":"link"}