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E-BookEPUB2 - DRM Adobe / EPUBE-Book
626 Seiten
Englisch
Wiley-VCHerschienen am10.07.20153. vollständig überarbeitete Auflage
The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.
This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations.

The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).
 

Rubin H. Landau is Professor Emeritus in the Department of Physics at Oregon State University in Corvallis. He has been teaching courses in computational physics for over 25 years, was a founder of the Computational Physics Degree Program and the Northwest Alliance for Computational Science and Engineering, and has been using computers in theoretical physics research ever since graduate school. He is author of more than 90 refereed publications and has also authored books on Quantum Mechanics, Workstations and Supercomputers, the first two editions of Computational Physics, and a First Course in Scientific Computing.
Manuel J. Paez is a professor in the Department of Physics at the University of Antioquia in Medellin, Colombia. He has been teaching courses in Modern Physics, Nuclear Physics, Computational Physics, Mathematical Physics as well as programming in Fortran, Pascal and C languages. He and Professor Landau have conducted pioneering computational investigations in the interactions of mesons and nucleons with nuclei.
Cristian C. Bordeianu teaches Physics and Computer Science at the Military College '?tefan cel Mare' in Campulung Moldovenesc, Romania. He has over twenty years of experience in developing educational software for high school and university curricula. He is winner of the 2008 Undergraduate Computational Engineering and Science Award by the US Department of Energy and the Krell Institute. His current research interests include chaotic dynamics in nuclear multifragmentation and plasma of quarks and gluons.mehr

Produkt

KlappentextThe use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.
This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations.

The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).
 

Rubin H. Landau is Professor Emeritus in the Department of Physics at Oregon State University in Corvallis. He has been teaching courses in computational physics for over 25 years, was a founder of the Computational Physics Degree Program and the Northwest Alliance for Computational Science and Engineering, and has been using computers in theoretical physics research ever since graduate school. He is author of more than 90 refereed publications and has also authored books on Quantum Mechanics, Workstations and Supercomputers, the first two editions of Computational Physics, and a First Course in Scientific Computing.
Manuel J. Paez is a professor in the Department of Physics at the University of Antioquia in Medellin, Colombia. He has been teaching courses in Modern Physics, Nuclear Physics, Computational Physics, Mathematical Physics as well as programming in Fortran, Pascal and C languages. He and Professor Landau have conducted pioneering computational investigations in the interactions of mesons and nucleons with nuclei.
Cristian C. Bordeianu teaches Physics and Computer Science at the Military College '?tefan cel Mare' in Campulung Moldovenesc, Romania. He has over twenty years of experience in developing educational software for high school and university curricula. He is winner of the 2008 Undergraduate Computational Engineering and Science Award by the US Department of Energy and the Krell Institute. His current research interests include chaotic dynamics in nuclear multifragmentation and plasma of quarks and gluons.
Details
Weitere ISBN/GTIN9783527684694
ProduktartE-Book
EinbandartE-Book
FormatEPUB
Format Hinweis2 - DRM Adobe / EPUB
FormatFormat mit automatischem Seitenumbruch (reflowable)
Verlag
Erscheinungsjahr2015
Erscheinungsdatum10.07.2015
Auflage3. vollständig überarbeitete Auflage
Seiten626 Seiten
SpracheEnglisch
Dateigrösse36228 Kbytes
Artikel-Nr.3207748
Rubriken
Genre9201

Inhalt/Kritik

Leseprobe
1
Introduction

Beginnings are hard.

Chaim Potok

Nothing is more expensive than a start.

Friedrich Nietzsche

This book is really two books. There is a rather traditional paper one with a related Web site, as well as an eBook version containing a variety of digital features best experienced on a computer. Yet even if you are reading from paper, you can still avail yourself of many of digital features, including video-based lecture modules, via the book's Web sites: http://physics.oregonstate.edu/~rubin/Books/CPbook/eBook/Lectures/ and www.wiley.com/WileyCDA.

We start this chapter with a description of how computational physics (CP) fits into physics and into the broader field of computational science. We then describe the subjects we are to cover, and present lists of all the problems in the text and in which area of physics they can be used as computational examples. The chapter finally gets down to business by discussing the Python language, some of the many packages that are available for Python, and some detailed examples of the use of visualization and symbolic manipulation packages.
1.1 Computational Physics and Computational Science

This book presents computational physics (CP) as a subfield of computational science. This implies that CP is a multidisciplinary subject that combines aspects of physics, applied mathematics, and computer science (CS) (Figure 1.1a), with the aim of solving realistic and ever-changing physics problems. Other computational sciences replace physics with their discipline, such as biology, chemistry, engineering, and so on. Although related, computational science is not part of computer science. CS studies computing for its own intrinsic interest and develops the hardware and software tools that computational scientists use. Likewise, applied mathematics develops and studies the algorithms that computational scientists use. As much as we also find math and CS interesting for their own sakes, our focus is on helping the reader do better physics for which you need to understand the CS and math well enough to solve your problems correctly, but not to become an expert programmer.

Figure 1.1 (a) A representation of the multi-disciplinary nature of computational physics as an overlap of physics, applied mathematics and computer science, and as a bridge among them. (b) Simulation has been added to experiment and theory as a basic approach in the search for scientific truth. Although this book focuses on simulation, we present it as part of the scientific process.

As CP has matured, we have come to realize that it is more than the overlap of physics, computer science, and mathematics. It is also a bridge among them (the central region in Figure 1.1a) containing core elements of it own, such as computational tools and methods. To us, CP s commonality of tools and its problem-solving mindset draws it toward the other computational sciences and away from the subspecialization found in so much of physics. In order to emphasize our computational science focus, to the extent possible, we present the subjects in this book in the form of a Problem to solve, with the components that constitute the solution separated according to the scientific problem-solving paradigm (Figure 1.1b). In recent times, this type of problem-solving approach, which can be traced back to the post-World War II research techniques developed at US national laboratories, has been applied to science education where it is called something like computational scientific thinking. This is clearly related to what the computer scientists more recently have come to call Computational Thinking, but the former is less discipline specific. Our computational scientific thinking is a hands-on, inquiry-based project approach in which there is problem analysis, a theoretical foundation that considers computability and appropriate modeling, algorithmic thinking and development, debugging, and an assessment that leads back to the original problem.

Traditionally, physics utilizes both experimental and theoretical approaches to discover scientific truth. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a mastery of the art of programming. The actual debugging, testing, and organization of scientific programs are analogous to experimentation, with the numerical simulations of nature being virtual experiments. The synthesis of numbers into generalizations, predictions, and conclusions requires the insight and intuition common to both experimental and theoretical science. In fact, the use of computation and simulation has now become so prevalent and essential a part of the scientific process that many people believe that the scientific paradigm has been extended to include simulation as an additional pillar (Figure 1.1b). Nevertheless, as a science, CP must hold experiment supreme, regardless of the beauty of the mathematics.
1.2 This Book s Subjects

This book starts with a discussion of Python as a computing environment and then discusses some basic computational topics. A simple review of computing hardware is put off until Chapter 10, although it also fits logically at the beginning of a course. We include some physics applications in the first third of this book, by put off most CP until the latter two-thirds of the book.

This text have been written to be accessible to upper division undergraduates, although many graduate students without a CP background might also benefit, even from the more elementary topics. We cover both ordinary and partial differential equation (PDE) applications, as well as problems using linear algebra, for which we recommend the established subroutine libraries. Some intermediate-level analysis tools such as discrete Fourier transforms, wavelet analysis, and singular value/principal component decompositions, often poorly understood by physics students, are also covered (and recommended). We also present various topics in fluid dynamics including shock and soliton physics, which in our experience physics students often do not see otherwise. Some more advanced topics include integral equations for both the bound state and (singular) scattering problem in quantum mechanics, as well as Feynman path integrations.

A traditional way to view the materials in this text is in terms of its use in courses. In our classes (CPUG, 2009), we have used approximately the first third of the text, with its emphasis on computing tools, for a course called Scientific Computing that is taken after students have acquired familiarity with some compiled language. Typical topics covered in this one-quarter course are given in Table 1.1, although we have used others as well. The latter two-thirds of the text, with its greater emphasis on physics, has typically been used for a two-quarter (20-week) course in CP. Typical topics covered for each quarter are given in Table 1.2. What with many of the topics being research level, these materials can easily be used for a full year s course or for extended research projects.

The text also uses various symbols and fonts to help clarify the type of material being dealt with. These include:
Optional material Monospace font Words as they would appear on a computer screen Vertical gray line Note to reader at the beginning of a chapter saying
Table 1.1 Topics for one-quarter (10 Weeks) scientific computing course.
Week Topics Chapter Week Topics Chapter 1 OS tools, limits 1, (10) 6 Matrices, N-D search 6 2 Visualization, Errors 1, 3 7 Data fitting 7 3 Monte Carlo, 4, 4 8 ODE oscillations 8 4 Integration, visualization 5, (1) 9 ODE eigenvalues 8 5 Derivatives, searching 5, 7 10 Hardware basics 10
Table 1.2 Topics for two-quarters (20 Weeks) computational physics course.
Computational Physics I Computational Physics II Week Topics Chapter Week Topics Chapter 1 Nonlinear ODEs 8, 9 1 Ising model, Metropolis 17 2 Chaotic scattering 9 2 Molecular dynamics 18 3 Fourier analysis, filters 12 3 Project completions - 4 Wavelet analysis 13 4 Laplace and Poisson PDEs 19 5 Nonlinear maps 14 5 Heat PDE 19 6 Chaotic/double...mehr