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Numerical methods in physics with Python

Gezerlis, Alex.

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Numerical methods in physics with Python

Record Type:	Electronic resources : Monograph/item
Title/Author:	Numerical methods in physics with Python/ Alex Gezerlis.
Author:	Gezerlis, Alex.
Published:	Cambridge :Cambridge University Press, : 2020.,
Description:	xviii, 586 p. :ill., digital ;24 cm.
Notes:	Title from publisher's bibliographic system (viewed on 14 Aug 2020).
[NT 15003449]:	Idiomatic Python -- Numbers -- Derivatives -- Matrices -- Roots -- Approximation -- Integrals -- Differential Equations.
Subject:	Mathematical physics - Data processing. -
Online resource:	https://doi.org/10.1017/9781108772310
ISBN:	9781108772310

Numerical methods in physics with Python
Gezerlis, Alex.

Numerical methods in physics with Python[electronic resource] /Alex Gezerlis. - Cambridge :Cambridge University Press,2020. - xviii, 586 p. :ill., digital ;24 cm.

Title from publisher's bibliographic system (viewed on 14 Aug 2020).

Idiomatic Python -- Numbers -- Derivatives -- Matrices -- Roots -- Approximation -- Integrals -- Differential Equations.

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. Accompanying the mathematical derivations are full implementations of dozens of numerical methods in Python, as well as more than 250 end-of-chapter problems. Numerical methods and physics examples are clearly separated, allowing this introductory book to be later used as a reference; the penultimate section in each chapter is an in depth project, tackling physics problems which 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: 9781108772310Subjects--Topical Terms:

534168
Mathematical physics
--Data processing.

LC Class. No.: QC20.7.N86 / G49 2020

Dewey Class. No.: 530.15028553

Numerical methods in physics with Python
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020 $a 9781108772310 $q (electronic bk.)
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050 0 0 $a QC20.7.N86 $b G49 2020
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100 1 $a Gezerlis, Alex. $3 3615947
245 1 0 $a Numerical methods in physics with Python $h [electronic resource] / $c Alex Gezerlis.
260 $a Cambridge : $b Cambridge University Press, $c 2020.
300 $a xviii, 586 p. : $b ill., digital ; $c 24 cm.
500 $a Title from publisher's bibliographic system (viewed on 14 Aug 2020).
505 0 $a Idiomatic Python -- Numbers -- Derivatives -- Matrices -- Roots -- Approximation -- Integrals -- Differential Equations.
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. Accompanying the mathematical derivations are full implementations of dozens of numerical methods in Python, as well as more than 250 end-of-chapter problems. Numerical methods and physics examples are clearly separated, allowing this introductory book to be later used as a reference; the penultimate section in each chapter is an in depth project, tackling physics problems which 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.
650 0 $a Mathematical physics $x Data processing. $3 534168
650 0 $a Numerical analysis $x Data processing. $3 532594
650 0 $a Python (Computer program language) $3 729789
856 4 0 $u https://doi.org/10.1017/9781108772310