Applied Numerical Methods With Python For Engineers And Scientists

"When we first learned to use computers as students in the 1960s, Fortran was the language of choice for most engineering and scientific computations. Over the ensuing half century, numerous other languages have proven useful for implementing the numerical calculations that are so valuable to our research and teaching. Along with a succession of improved Fortran versions, other languages such as Algol, Basic, Pascal, and C/C++ have all found their way into our computational toolbox. The basic content, organization, and pedagogy of this book is like our other numerical methods textbooks. In particular, a conversational writing style is intentionally maintained in order to make the book easier to read. This book tries to speak directly to the reader and is designed in part to be a tool for self-teaching. As such, we also believe it will have value outside the classroom for professionals desiring to gain proficiency in both numerical methods and Python"--

Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice Summaries at the end of each chapter allow for quick access to important information Includes code in Jupyter notebook format that can be directly run online

"When we first learned to use computers as students in the 1960s, Fortran was the language of choice for most engineering and scientific computations. Over the ensuing half century, numerous other languages have proven useful for implementing the numerical calculations that are so valuable to our research and teaching. Along with a succession of improved Fortran versions, other languages such as Algol, Basic, Pascal, and C/C++ have all found their way into our computational toolbox. The basic content, organization, and pedagogy of this book is like our other numerical methods textbooks. In particular, a conversational writing style is intentionally maintained in order to make the book easier to read. This book tries to speak directly to the reader and is designed in part to be a tool for self-teaching. As such, we also believe it will have value outside the classroom for professionals desiring to gain proficiency in both numerical methods and Python"--

The book is specifically intended for scientists, engineers, and engineering students who have taken a course on numeric methods and wish to comprehend and learn the subject through programming. The book's chapters are written methodically (step-by-step) so that programming becomes simple. More emphasis is placed on computationally modelling the methodologies and discussing the numerical method. Python is chosen as the programming language because it is simple to comprehend and use compared to other programming languages. The book allows readers to use and experiment with the approaches it describes. With very few adjustments, many of the programmes in the book can be utilised for applications in science and engineering.

Still brief - but with the chapters that you wanted - Steven Chapra’s new second edition is written for engineering and science students who need to learn numerical problem solving. This text focuses on problem-solving applications rather than theory, using MATLAB throughout. Theory is introduced to inform key concepts which are framed in applications and demonstrated using MATLAB. The new second edition feature new chapters on Numerical Differentiation, Optimization, and Boundary-Value Problems (ODEs).

Provides an introduction to numerical methods for students in engineering. It uses Python 3, an easy-to-use, high-level programming language.

Makes Numerical Programming More Accessible to a Wider Audience Bearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author’s many years of practical research and teaching experience to offer a systematic approach to relevant programming concepts. Adopting a practical, broad appeal, this user-friendly book offers guidance to anyone interested in using numerical programming to solve science and engineering problems. Emphasizing methods generally used in physics and engineering—from elementary methods to complex algorithms—it gradually incorporates algorithmic elements with increasing complexity. Develop a Combination of Theoretical Knowledge, Efficient Analysis Skills, and Code Design Know-How The book encourages algorithmic thinking, which is essential to numerical analysis. Establishing the fundamental numerical methods, application numerical behavior and graphical output needed to foster algorithmic reasoning, coding dexterity, and a scientific programming style, it enables readers to successfully navigate relevant algorithms, understand coding design, and develop efficient programming skills. The book incorporates real code, and includes examples and problem sets to assist in hands-on learning. Begins with an overview on approximate numbers and programming in Python and C/C++, followed by discussion of basic sorting and indexing methods, as well as portable graphic functionality Contains methods for function evaluation, solving algebraic and transcendental equations, systems of linear algebraic equations, ordinary differential equations, and eigenvalue problems Addresses approximation of tabulated functions, regression, integration of one- and multi-dimensional functions by classical and Gaussian quadratures, Monte Carlo integration techniques, generation of random variables, discretization methods for ordinary and partial differential equations, and stability analysis This text introduces platform-independent numerical programming using Python and C/C++, and appeals to advanced undergraduate and graduate students in natural sciences and engineering, researchers involved in scientific computing, and engineers carrying out applicative calculations.