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Bayesian optimization with applicati...

Pourmohamad, Tony.

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Bayesian optimization with application to computer experiments

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian optimization with application to computer experiments/ by Tony Pourmohamad, Herbert K. H. Lee.
Author:	Pourmohamad, Tony.
other author:	Lee, Herbert K. H.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	x, 104 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	1. Computer experiments -- 2. Surrogate models -- 3. Unconstrained optimization -- 4. Constrained optimization.
Contained By:	Springer Nature eBook
Subject:	Experimental design. -
Online resource:	https://doi.org/10.1007/978-3-030-82458-7
ISBN:	9783030824587

Bayesian optimization with application to computer experiments
Pourmohamad, Tony.

Bayesian optimization with application to computer experiments[electronic resource] /by Tony Pourmohamad, Herbert K. H. Lee. - Cham :Springer International Publishing :2021. - x, 104 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in statistics,2191-5458. - SpringerBriefs in statistics..

1. Computer experiments -- 2. Surrogate models -- 3. Unconstrained optimization -- 4. Constrained optimization.

This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.

ISBN: 9783030824587

Standard No.: 10.1007/978-3-030-82458-7doiSubjects--Topical Terms:

524737
Experimental design.

LC Class. No.: QA279 / .P68 2021

Dewey Class. No.: 519.5

Bayesian optimization with application to computer experiments
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245 1 0 $a Bayesian optimization with application to computer experiments $h [electronic resource] / $c by Tony Pourmohamad, Herbert K. H. Lee.
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300 $a x, 104 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, $x 2191-5458
505 0 $a 1. Computer experiments -- 2. Surrogate models -- 3. Unconstrained optimization -- 4. Constrained optimization.
520 $a This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.
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