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Numerical methods in physics with Python

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Numerical methods in physics with PythonAlex Gezerlis, University of Guelph.
作者:	Gezerlis, Alex.
出版者:	Cambridge, United Kingdom ;Cambridge University Press,2023.
面頁冊數:	xvi, 688 p. :ill., digital ;26 cm.
附註:	Title from publisher's bibliographic system (viewed on 30 Aug 2023).
標題:	Mathematical physicsData processing.
電子資源:	https://doi.org/10.1017/9781009303897
ISBN:	9781009303897

Numerical methods in physics with Python
Gezerlis, Alex.

Numerical methods in physics with Python[electronic resource] /Alex Gezerlis, University of Guelph. - Second edition. - Cambridge, United Kingdom ;Cambridge University Press,2023. - xvi, 688 p. :ill., digital ;26 cm.

Title from publisher's bibliographic system (viewed on 30 Aug 2023).

Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.

ISBN: 9781009303897Subjects--Topical Terms:

211453
Mathematical physics
--Data processing.

LC Class. No.: QC20.7.N86 / G49 2023

Dewey Class. No.: 530.15028553

Numerical methods in physics with Python
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500 $a Title from publisher's bibliographic system (viewed on 30 Aug 2023).
520 $a Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.
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