Description

Book Synopsis

Mathematical Modeling: Branching Beyond Calculus reveals the versatility of mathematical modeling. The authors present the subject in an attractive manner and flexibley manner. Students will discover that the topic not only focuses on math, but biology, engineering, and both social and physical sciences.

The book is written in a way to meet the needs of any modeling course. Each chapter includes examples, exercises, and projects offering opportunities for more in-depth investigations into the world of mathematical models. The authors encourage students to approach the models from various angles while creating a more complete understanding. The assortment of disciplines covered within the book and its flexible structure produce an intriguing and promising foundation for any mathematical modeling course or for self-study.

Key Features:

  • Chapter projects guide more thorough investigations of the models
  • The text aims

    Trade Review

    Undergraduate textbooks on calculus, differential equations, and linear algebra usually contain a few exercises per chapter that use their subject to model a phenomenon from outside mathematics—typically from physics, biology, chemistry, engineering, or economics. In a typical class, these applications do not amount to more than ten percent of class time. In this book, the authors collect modeling examples from those three areas and make them the central focus of their book. For most of the book, no new theory is covered; instead, the authors provide brief refreshers on some of the necessary theoretical concepts from calculus, differential equations, and linear algebra. The intended audience is second- or third-year students who have already taken those classes. A few exercises accompany each section, with solutions included at the end of the book. The fifth and last chapter does contain material that will be new to most mid-career undergraduates, such as Monte-Carlo simulations and the Prisoners' Dilemma. This book seems ideally suited to an undergraduate class on modeling—a class that few institutions likely offer—and may serve some as a means of independent study.
    --M. Bona, University of Florida



    Table of Contents

    Chapter 1: Modeling with Calculus; Exploring Extrema; Modeling with The Fundamental Theorem of Calculus; Probability Distributions; Introduction to Stochastic Processes Applications of Sequences and Series; Fibonacci and Lucas Sequences; Taylor Approximations Fourier Series and Signal Processing. Chapter 2: Modeling with Linear Algebra; Modeling with Graphs; Stochastic Models - Markov Chains; Leslie Matrices and other Matrix Models; Linear Programming; Game Theory. Chapter 3: Modeling with Programming; Simulations; Automata Models; Branching Theory. Chapter 4: Modeling with Ordinary Differential Equations; Introduction of Modeling with Differential Equations and Difference Equations; Basic Growth Models; Finding and Analyzing Equilibrium; Multiple Population Models, Coupled Systems; Epidemic Models; Models in a Variety of Fields.

Mathematical Modeling

Product form

£87.39

Includes FREE delivery

RRP £91.99 – you save £4.60 (5%)

Order before 4pm tomorrow for delivery by Tue 31 Mar 2026.

A Hardback by Crista Arangala, Nicolas S. Luke, Karen A. Yokley

Out of stock


    View other formats and editions of Mathematical Modeling by Crista Arangala

    Publisher: Taylor & Francis Inc
    Publication Date: 1/6/2018 12:02:00 AM
    ISBN13: 9781498770712, 978-1498770712
    ISBN10: 1498770711

    Description

    Book Synopsis

    Mathematical Modeling: Branching Beyond Calculus reveals the versatility of mathematical modeling. The authors present the subject in an attractive manner and flexibley manner. Students will discover that the topic not only focuses on math, but biology, engineering, and both social and physical sciences.

    The book is written in a way to meet the needs of any modeling course. Each chapter includes examples, exercises, and projects offering opportunities for more in-depth investigations into the world of mathematical models. The authors encourage students to approach the models from various angles while creating a more complete understanding. The assortment of disciplines covered within the book and its flexible structure produce an intriguing and promising foundation for any mathematical modeling course or for self-study.

    Key Features:

    • Chapter projects guide more thorough investigations of the models
    • The text aims

      Trade Review

      Undergraduate textbooks on calculus, differential equations, and linear algebra usually contain a few exercises per chapter that use their subject to model a phenomenon from outside mathematics—typically from physics, biology, chemistry, engineering, or economics. In a typical class, these applications do not amount to more than ten percent of class time. In this book, the authors collect modeling examples from those three areas and make them the central focus of their book. For most of the book, no new theory is covered; instead, the authors provide brief refreshers on some of the necessary theoretical concepts from calculus, differential equations, and linear algebra. The intended audience is second- or third-year students who have already taken those classes. A few exercises accompany each section, with solutions included at the end of the book. The fifth and last chapter does contain material that will be new to most mid-career undergraduates, such as Monte-Carlo simulations and the Prisoners' Dilemma. This book seems ideally suited to an undergraduate class on modeling—a class that few institutions likely offer—and may serve some as a means of independent study.
      --M. Bona, University of Florida



      Table of Contents

      Chapter 1: Modeling with Calculus; Exploring Extrema; Modeling with The Fundamental Theorem of Calculus; Probability Distributions; Introduction to Stochastic Processes Applications of Sequences and Series; Fibonacci and Lucas Sequences; Taylor Approximations Fourier Series and Signal Processing. Chapter 2: Modeling with Linear Algebra; Modeling with Graphs; Stochastic Models - Markov Chains; Leslie Matrices and other Matrix Models; Linear Programming; Game Theory. Chapter 3: Modeling with Programming; Simulations; Automata Models; Branching Theory. Chapter 4: Modeling with Ordinary Differential Equations; Introduction of Modeling with Differential Equations and Difference Equations; Basic Growth Models; Finding and Analyzing Equilibrium; Multiple Population Models, Coupled Systems; Epidemic Models; Models in a Variety of Fields.

    Recently viewed products

    © 2026 Book Curl

      • American Express
      • Apple Pay
      • Diners Club
      • Discover
      • Google Pay
      • Maestro
      • Mastercard
      • PayPal
      • Shop Pay
      • Union Pay
      • Visa

      Login

      Forgot your password?

      Don't have an account yet?
      Create account