Computation in Science

Computation in Science

Konrad Hinsen
ISBN: 9781681740294 | PDF ISBN: 9781681740935
Copyright © 2016 | 135 Pages | Publication Date: December 17, 2015

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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 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.

Table of Contents

1. What is computation?
2. Computation in science
3. Formalizing computation
4. Automating computation
5. Taming complexity
6. Outlook: Scientific knowledge in the digital age

About the Author(s)

Konrad Hinsen, Centre National de la Recherche Scientifique (CNRS)
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.

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