Description

Book Synopsis

This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python.

Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge–Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing.

Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science.



Trade Review

“This book is a valuable contribution, easily readable, in the field of computation of linear and nonlinear systems using Python. … The book will be very useful to a vast number of readers in various fields.” (Nirode C. Mohanty, zbMATH 1411.65004, 2019)




Table of Contents

Motivation and Background

Number Representations and Errors

Numerical Calculus

Linear Equations

Iterative Solution of Nonlinear Equations

Interpolation

Differential Equations

Applied Scientific Computing: With Python

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RRP £44.99 – you save £4.50 (10%)

Order before 4pm today for delivery by Tue 20 Jan 2026.

A Hardback by Peter R. Turner, Thomas Arildsen, Kathleen Kavanagh

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    View other formats and editions of Applied Scientific Computing: With Python by Peter R. Turner

    Publisher: Springer International Publishing AG
    Publication Date: 31/07/2018
    ISBN13: 9783319895741, 978-3319895741
    ISBN10: 3319895745

    Description

    Book Synopsis

    This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python.

    Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge–Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing.

    Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science.



    Trade Review

    “This book is a valuable contribution, easily readable, in the field of computation of linear and nonlinear systems using Python. … The book will be very useful to a vast number of readers in various fields.” (Nirode C. Mohanty, zbMATH 1411.65004, 2019)




    Table of Contents

    Motivation and Background

    Number Representations and Errors

    Numerical Calculus

    Linear Equations

    Iterative Solution of Nonlinear Equations

    Interpolation

    Differential Equations

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