"Version: 20151201"--Title page verso.

"A Morgan & Claypool publication as part of IOP Concise Physics"--Title page verso.

Includes bibliographical references.

Preface -- 1. What is computation? -- 1.1. Defining computation -- 1.2. The roles of computation in scientific research -- 1.3. Further reading

2. Computation in science -- 2.1. Traditional science : celestial mechanics -- 2.2. Scientific models and computation -- 2.3. Computation at the interface between observations and models -- 2.4. Computation for developing insight -- 2.5. The impact of computing on science -- 2.6. Further reading

3. Formalizing computation -- 3.1. From manual computation to rewriting rules -- 3.2. From computing machines to automata theory -- 3.3. Computability -- 3.4. Restricted models of computation -- 3.5. Computational complexity -- 3.6. Computing with numbers -- 3.7. Further reading

4. Automating computation -- 4.1. Computer architectures -- 4.2. Programming languages -- 4.3. Software engineering -- 4.4. Further reading

5. Taming complexity -- 5.1. Chaos and complexity in computation -- 5.2. Validation and testing -- 5.3. Abstraction -- 5.4. Managing state -- 5.5. Incidental complexity and technical debt -- 5.6. Further reading

6. Outlook : scientific knowledge in the digital age -- 6.1. Software as a medium for representing scientific knowledge -- 6.2. Reproducibility -- 6.3. The time scales of scientific progress and computing -- 6.4. Preparing the future -- 6.5. Further reading.

Computation in Science provides a theoretical background in computation to scientists who use computational methods. It explains how computing is used in the natural sciences, and provides a high-level overview of those aspects of computer science and software engineering that are most relevant for computational science. The focus is on concepts, results, and applications, rather than on proofs and derivations. The unique feature of this book is that it connects the dots between computational science, the theory of computation and information, and software engineering. It should help scientists to better understand how they use computers in their work, and to how computers work. It is meant to compensate for the general lack of any formal training in computer science and information theory. Readers will learn something that they can use throughout their careers.

Graduate and postgraduate professional researchers and engineers.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Konrad Hinsen obtained a PhD in theoretical physics from RWTH Aachen University. He has been a researcher at the French Centre National de la Recherche Scientifique (CNRS) for 15 years and he is the author or co-author of 70 scientific publications in the fields of colloid science, molecular biophysics, structural biology, and scientific computing. He was a founding member of the team that created the "Numerical Python" library, which became the basis for the highly successful scientific software ecosystem around the Python language. His current research interests are the development of coarse-grained models for protein structure, flexibility, and dynamics, and of techniques to improve the validation and replicability of computational science.

Title from PDF title page (viewed on January 10, 2016).