Scientific Computing With Case Studies

This book is a practical guide to the numerical solution of linear and nonlinear equations, differential equations, optimization problems, and eigenvalue problems. It treats standard problems and introduces important variants such as sparse systems, differential-algebraic equations, constrained optimization, Monte Carlo simulations, and parametric studies. Stability and error analysis are emphasized, and the Matlab algorithms are grounded in sound principles of software design and understanding of machine arithmetic and memory management. Nineteen case studies provide experience in mathematical modeling and algorithm design, motivated by problems in physics, engineering, epidemiology, chemistry, and biology. The topics included go well beyond the standard first-course syllabus, introducing important problems such as differential-algebraic equations and conic optimization problems, and important solution techniques such as continuation methods. The case studies cover a wide variety of fascinating applications, from modeling the spread of an epidemic to determining truss configurations.

A complete guide for Python programmers to master scientific computing using Python APIs and tools About This Book The basics of scientific computing to advanced concepts involving parallel and large scale computation are all covered. Most of the Python APIs and tools used in scientific computing are discussed in detail The concepts are discussed with suitable example programs Who This Book Is For If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming. What You Will Learn Fundamentals and components of scientific computing Scientific computing data management Performing numerical computing using NumPy and SciPy Concepts and programming for symbolic computing using SymPy Using the plotting library matplotlib for data visualization Data analysis and visualization using Pandas, matplotlib, and IPython Performing parallel and high performance computing Real-life case studies and best practices of scientific computing In Detail In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing. At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python. The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs. Style and approach This book follows a hands-on approach to explain the complex concepts related to scientific computing. It details various APIs using appropriate examples.

Scientific Computing with Automatic Result Verification

Computer Science and Scientific Computing contains the proceedings of the Third ICASE Conference on Scientific Computing held in Williamsburg, Virginia, on April l and 2, 1976, under the auspices of the Institute for Computer Applications in Systems Engineering at the NASA Langley Research Center. The conference provided a forum for reviewing all the aspects of scientific computing and covered topics ranging from computer-aided design (CAD) and computer science technology to the design of large hydrodynamics codes. Case studies in reliable computing are also presented. Comprised of 13 chapters, this book begins with an introduction to the use of the hierarchical family concept in the development of scientific programming systems. The discussion then turns to the data structures of scientific computing and their representation and management; some important CAD capabilities required to support aerospace design in the areas of interactive support, information management, and computer hardware advances as well as some computer science developments which may contribute significantly to making such capabilities possible; and the use of symbolic computation systems for problem solving in scientific research. Subsequent chapters deal with computer applications in astrophysics; the possibility of computing turbulence and numerical wind tunnels; and the basis for a general-purpose program for finite element analysis. Software tools for computer graphics are also considered. This monograph will be of value to scientists, systems designers and engineers, and students in computer science who have an interest in the subject of scientific computing.

Summary: This work combines selected papers from a July 2008 workshop held in Cetraro, Italy, with invited papers by international contributors. Material is in sections on algorithms and scheduling, architectures, GRID technologies, cloud technologies, information processing and applications, and HPC and GRID infrastructures for e-science. B&w maps, images, and screenshots are used to illustrate topics such as nondeterministic coordination using S-Net, cloud computing for on-demand grid resource provisioning, grid computing for financial applications, and the evolution of research and education networks and their essential role in modern science. There is no subject index. The book's readership includes computer scientists, IT engineers, and managers interested in the future development of grids, clouds, and large-scale computing. Gentzsch is affiliated with the DEISA Project and Open Grid Forum, Germany.

An essential contribution to the study of the history of computers, this work identifies the computer's impact on the physical, biological, cognitive, and medical sciences. References fundamental to the understudied area of the history of scientific computing also document the significant role of the sciences in helping to shape the development of computer technology. More broadly, the many resources on scientific computing help demonstrate how the computer was the most significant scientific instrument of the 20th century. The only guide of its kind covering the use and impact of computers on the the physical, biological, medical, and cognitive sciences, it contains more than 1,000 annotated citations to carefully selected secondary and primary resources. Historians of technology and science will find this a very useful resource. Computer scientists, physicians, biologists, chemists, and geologists will also benefit from this extensive bibliography on the history of computer applications and the sciences.

This is the first of three volumes providing a comprehensive presentation of the fundamentals of scientific computing. This volume discusses basic principles of computation, and fundamental numerical algorithms that will serve as basic tools for the subsequent two volumes. This book and its companions show how to determine the quality of computational results, and how to measure the relative efficiency of competing methods. Readers learn how to determine the maximum attainable accuracy of algorithms, and how to select the best method for computing problems. This book also discusses programming in several languages, including C++, Fortran and MATLAB. There are 80 examples, 324 exercises, 77 algorithms, 35 interactive JavaScript programs, 391 references to software programs and 4 case studies. Topics are introduced with goals, literature references and links to public software. There are descriptions of the current algorithms in LAPACK, GSLIB and MATLAB. This book could be used for an introductory course in numerical methods, for either upper level undergraduates or first year graduate students. Parts of the text could be used for specialized courses, such as principles of computer languages or numerical linear algebra.

While the extensible markup language (XML) has received a great deal of attention in web programming and software engineering, far less attention has been paid to XML in mainstream computational science and engineering. Correcting this imbalance, XML in Scientific Computing introduces XML to scientists and engineers in a way that illustrates the similarities and differences with traditional programming languages and suggests new ways of saving and sharing the results of scientific calculations. The author discusses XML in the context of scientific computing, demonstrates how the extensible stylesheet language (XSL) can be used to perform various calculations, and explains how to create and navigate through XML documents using traditional languages such as Fortran, C++, and MATLAB®. A suite of computer programs are available on the author’s website.