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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…mehr
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
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Autorenporträt
Qingkai Kong is an Assistant Data Science Researcher at the Berkeley Division of Data Sciences and Berkeley Seismology Lab. He has a Master's degree in Structural Engineering and a PhD. in Earth Science. He is actively working on applying data science/machine learning to Earth science and engineering, especially using Python language.Alexandre Bayen is the Liao-Cho Professor of Engineering at UC Berkeley. He is a Professor of Electrical Engineering and Computer Science, and Civil and Environmental Engineering. He is currently the Director of the Institute of Transportation Studies (ITS). He is also a Faculty Scientist in Mechanical Engineering, at the Lawrence Berkeley National Laboratory (LBNL). He received the Engineering Degree in applied mathematics from the Ecole Polytechnique, France, in 1998, the M.S. and Ph.D. in aeronautics and astronautics from Stanford University in 1998 and 1999 respectively. He was a Visiting Researcher at NASA Ames Research Center from 2000 to 2003. Between January 2004 and December 2004, he worked as the Research Director of the Autonomous Navigation Laboratory at the Laboratoire de Recherches Balistiques et Aerodynamiques, (Ministere de la Defense, Vernon, France), where he holds the rank of Major. He has been on the faculty at UC Berkeley since 2005. Bayen has authored two books and over 200 articles in peer reviewed journals and conferences. He is the recipient of the Ballhaus Award from Stanford University, 2004, of the CAREER award from the National Science Foundation, 2009 and he is a NASA Top 10 Innovators on Water Sustainability, 2010. His projects Mobile Century and Mobile Millennium received the 2008 Best of ITS Award for 'Best Innovative Practice', at the ITS World Congress and a TRANNY Award from the California Transportation Foundation, 2009. Mobile Millennium has been featured more than 200 times in the media, including TV channels and radio stations (CBS, NBC, ABC, CNET, NPR, KGO, the BBC), and in the popular press (Wall Street Journal, Washington Post, LA Times). Bayen is the recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) award from the White House, 2010. He is also the recipient of the Okawa Research Grant Award, the Ruberti Prize from the IEEE, and the Huber Prize from the ASCE.
Inhaltsangabe
PART 1 INTRODUCTION TO PYTHON PROGRAMMING CHAPTER 1 Python Basics CHAPTER 2 Variables and Basic Data Structures CHAPTER 3 Functions CHAPTER 4 Branching Statements CHAPTER 5 Iteration CHAPTER 7 Object-Oriented Programming CHAPTER 8 Complexity CHAPTER 9 Representation of Numbers CHAPTER 10 Errors, Good Programming Practices, and Debugging CHAPTER 11 Reading and Writing Data CHAPTER 12 Visualization and Plotting CHAPTER 13 Parallelize Your Python PART 2 INTRODUCTION TO NUMERICAL METHODS CHAPTER 14 Linear Algebra and Systems of Linear Equations CHAPTER 15 Eigenvalues and Eigenvectors CHAPTER 16 Least Squares Regression CHAPTER 17 Interpolation CHAPTER 18 Taylor Series CHAPTER 19 Root Finding CHAPTER 20 Numerical Differentiation CHAPTER 21 Numerical Integration CHAPTER 22 Ordinary Differential Equations (ODEs) Initial-Value Problems CHAPTER 23 Boundary-Value Problems for Ordinary Differential Equations (ODEs) CHAPTER 24 Fourier Transform
