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Brown D. Introduction to Scientific Computation. A First Course for...2026

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Brown D. Introduction to Scientific Computation. A First Course for...2026

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Category: Other
Total size: 8.08 MB
Added: 5 days ago (2025-11-05 12:58:01)

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Info Hash: E976E391617B8454B28E07C8333A067E0BB0005F
Last updated: 10 hours ago (2025-11-10 22:03:16)

Description:

Textbook in PDF format This book introduces scientific computation in an accessible and interactive way for undergraduates, especially those in the sciences, engineering, and mathematics. No prior programming experience is expected. Readers can begin directly from the introductory chapters, which cover basic elements of the Python programming language, then progress to the main chapters where techniques of scientific computation are presented and applied to specific problems. Examples and exercises are included in detail, covering diverse topics from various branches of science. These include cellular automata, the driven damped pendulum, Euler–Bernoulli beam theory, Newton's law of cooling, and many others. Rather than using established routines as a black box to memorize, this book delves into foundational concepts and encourages readers to apply fundamental principles to hands-on problems. Extensive use of scientific Python libraries and Jupyter Notebooks reinforce the student's understanding. This book is well-suited for a first exposure to scientific computation, but also to bridge introductory programming classes with advanced coursework in scientific modeling. The first nine chapters cover some basic elements of the Python programming language, the elements that are needed for scientific computing. Chapter 10 provides an introduction to symbolic computation. The remaining chapters present numerical techniques for solving the types of problems that commonly appear in science, engineering and math: root finding, curve fitting, interpolation, numerical integration, linear algebra, numerical differentiation, ordinary and partial differential equations, and Fourier analysis. The algorithms discussed in this book can be implemented in almost any computer language. We will use Python because Python is powerful, easy to learn, well-supported, and free. This book is intended to be interactive. Exercises are scattered throughout each chapter, and the reader is encouraged to tackle the exercises as they work through the material. Most of the exercises are designed to clarify and reinforce the material as the material is presented, not afterwards