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This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.
Computational Science and Engineering  Numerical Analysis
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This text provides a very simple, initial introduction to the complete scientific computing pipeline: models, discretization, algorithms, programming, verification, and visualization. The pedagogical strategy is to use one case study – an ordinary differential equation describing exponential decay processes – to illustrate fundamental concepts in mathematics and computer science. The book is easy to read and only requires a command of onevariable calculus and some very basic knowledge about computer programming. Contrary to similar texts on numerical methods and programming, this text has a much stronger focus on implementation and teaches testing and software engineering in particular.
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Flow of ions through voltage gated channels can be represented theoretically using stochastic differential equations where the gating mechanism is represented by a Markov model. The flow through a channel can be manipulated using various drugs, and the effect of a given drug can be reflected by changing the Markov model. These lecture notes provide an accessible introduction to the mathematical methods needed to deal with these models. They emphasize the use of numerical methods and provide sufficient details for the reader to implement the models and thereby study the effect of various drugs. Examples in the text include stochastic calcium release from internal storage systems in cells, as well as stochastic models of the transmembrane potential. Well known Markov models are studied and a systematic approach to including the effect of mutations is presented. Lastly, the book shows how to derive the optimal properties of a theoretical model of a drug for a given mutation defined in terms of a Markov model.
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This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and examplebased, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and objectoriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.
Computational Science and Engineering  Professional Computing  Programming Techniques  Software Engineering/Programming and Operating Systems  Mathematics of Computing  Programming Language  Software Engineering  programming  Python  computational science  data science  object oriented programming  open access  Maths for scientists  Applied computing  Computer programming / software engineering  Operating systems  Mathematical theory of computation  Maths for computer scientists
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This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 2123, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational subfields in computer science, mathematics, physics, or engineering will find it of particular interest.
Simulation and Modeling  Performance and Reliability  Computational Science and Engineering  Input/Output and Data Communications  Mathematical and Computational Engineering  Numerical and Computational Physics, Simulation  Computer Science  Computer Hardware  Mathematical and Computational Engineering Applications  Theoretical, Mathematical and Computational Physics  open access  computational algorithms and numerical methods  data management and exploration  highperformance computing  simulation software and applications  system software and software tools  Computer modelling & simulation  Systems analysis & design  Maintenance & repairs  Maths for scientists  Computer networking & communications  Distributed databases  Maths for engineers  Mathematical physics
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