Titlebook: Simulation-Driven Design by Knowledge-Based Response Correction Techniques; Slawomir Koziel,Leifur Leifsson Book 2016 Springer Internation

词汇表

amphibian

GRAVE

Book 2016lizing physics-based low-fidelity models, often based on coarse-discretization simulations or other types of simplified physics representations, such as analytical models. The methods presented in the book exploit as much as possible any knowledge about the system or device of interest embedded in t

Corroborate

Fundamentals of Numerical Optimization,-based approaches where most of the operations are carried out using a fast surrogate). In this chapter, we provide an outline and a brief overview of conventional optimization techniques, including gradient-based and derivative-free methods, as well as metaheuristics.

自恋

Introduction to Surrogate Modeling and Surrogate-Based Optimization,m of SBO exploiting the two aforementioned classes of models. More detailed information about the selected types of SBO algorithms (especially those involving response correction techniques) as well as illustration and application examples in various fields of engineering are provided in the remaining part of the book.

新义

Surrogate-Based Optimization Using Parametric Response Correction,nd/or solving simple (usually linear) regression problems. A simple example of a parametric response correction is the AMMO algorithm (cf. Sect. .), other examples can be found in Sect. .. Here, we focus on the methods working with vector-valued responses, such as output space mapping, manifold mapping, and multi-point response correction.

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上坡

Simulation-Driven Design,oughout the book, discuss typical design objectives and constraints, as well as describe common challenges, mostly related to the high computational cost of evaluating simulation models of the devices and systems under consideration. A brief outline of conventional numerical optimization methods is

鸽子

Fundamentals of Numerical Optimization,we provide—for the sake of making the material self-contained—some basic information about conventional optimization algorithms. In this book, we refer to conventional (or direct) methods as those that handle the expensive simulation model directly in the optimization scheme (as opposed to surrogate

有害处

Introduction to Surrogate Modeling and Surrogate-Based Optimization,ecall the SBO concept and the optimization flow, discuss the principles of surrogate modeling and typical approaches to construct surrogate models. We also discuss the distinction between function approximation (or data-driven) surrogates and physics-based surrogates, as well as outline the algorith