國立虎尾科技大學 |

Numerical methods in physics with Python /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Numerical methods in physics with Python // Alex Gezerlis.
Author:	Gezerlis, Alex,
Description:	1 online resource (xviii, 586 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 14 Aug 2020).
Subject:	Mathematical physics - Data processing. -
Online resource:	https://doi.org/10.1017/9781108772310
ISBN:	9781108772310 (ebook)

Numerical methods in physics with Python /
Gezerlis, Alex,

Numerical methods in physics with Python /Alex Gezerlis. - 1 online resource (xviii, 586 pages) :digital, PDF file(s).

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: 9781108772310 (ebook)Subjects--Topical Terms:

595762
Mathematical physics
--Data processing.

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

Dewey Class. No.: 530.150285/53

Numerical methods in physics with Python /
LDR:02175nam a2200301 i 4500 001 1124114
003 UkCbUP
005 20200818154408.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2020||||enk o ||1 0|eng|d
020 $a 9781108772310 (ebook)
020 $z 9781108488846 (hardback)
020 $z 9781108738934 (paperback)
035 $a CR9781108772310
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 0 0 $a QC20.7.N86 $b G49 2020
082 0 0 $a 530.150285/53 $2 23
100 1 $a Gezerlis, Alex, $e author. $3 1441453
245 1 0 $a Numerical methods in physics with Python / $c Alex Gezerlis.
264 1 $a Cambridge : $b Cambridge University Press, $c 2020.
300 $a 1 online resource (xviii, 586 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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 595762
650 0 $a Numerical analysis $x Data processing. $3 528215
650 0 $a Python (Computer program language) $3 566246
776 0 8 $i Print version: $z 9781108488846
856 4 0 $u https://doi.org/10.1017/9781108772310