| Computation in Science Contributor(s): Hinsen, Konrad (Author) |
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ISBN: 168174029X ISBN-13: 9781681740294 Publisher: Morgan & Claypool OUR PRICE: $38.00 Product Type: Paperback - Other Formats Published: December 2015 |
| Additional Information |
| BISAC Categories: - Science | Physics - General |
| Series: Iop Concise Physics |
| Physical Information: 0.24" H x 7" W x 10" (0.47 lbs) 135 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: This book 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. The book should help scientists to better understand how they use computers in their work, and to better understand how computers work. It is meant to compensate a bit for the general lack of any formal training in computer science and information theory. Readers will learn something they can use throughout their careers. |
Contributor Bio(s): Hinsen, Konrad: - 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. |