Scientific Computing
eBook - ePub

Scientific Computing

For Scientists and Engineers

  1. 184 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Scientific Computing

For Scientists and Engineers

About this book

Scientific Computing for Scientists and Engineers is designed to teach undergraduate students relevant numerical methods and required fundamentals in scientific computing.

Most problems in science and engineering require the solution of mathematical problems, most of which can only be done on a computer. Accurately approximating those problems requires solving differential equations and linear systems with millions of unknowns, and smart algorithms can be used on computers to reduce calculation times from years to minutes or even seconds. This book explains: How can we approximate these important mathematical processes? How accurate are our approximations? How efficient are our approximations?

Scientific Computing for Scientists and Engineers covers:

  • An introduction to a wide range of numerical methods for linear systems, eigenvalue problems, differential equations, numerical integration, and nonlinear problems;
  • Scientific computing fundamentals like floating point representation of numbers and convergence;
  • Analysis of accuracy and efficiency;
  • Simple programming examples in MATLAB to illustrate the algorithms and to solve real life problems;
  • Exercises to reinforce all topics.

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Yes, you can access Scientific Computing by Timo Heister,Leo G. Rebholz in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Title Page
  2. Copyright
  3. Contents
  4. Preface
  5. 1 Introduction
  6. 2 Computer representation of numbers and roundoff error
  7. 3 Solving linear systems of equations
  8. 4 Finite difference methods
  9. 5 Solving nonlinear equations
  10. 6 Accuracy in solving linear systems
  11. 7 Eigenvalues and eigenvectors
  12. 8 Fitting curves to data
  13. 9 Numerical integration
  14. 10 Initial value ODEs
  15. Subject Index