Numerical Solution Of Partial Differential Equations On Parallel Computers (Lecture Notes In Computational Science And Engineering)

Are Magnus Bruaset

ISBN 10: 3540290761 / ISBN 13: 9783540290766

Published by Springer, 2006

About this Item

Ancien livre de bibliothèque. Edition 2006. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2006. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Bibliographic Details

• Title: Numerical Solution Of Partial Differential Equations On Parallel Computers (Lecture Notes In Computational Science And Engineering)
• Publisher: Springer
• Publication Date: 2006
• Language: English
• ISBN 10: 3540290761
• ISBN 13: 9783540290766
• Binding: Softcover
• Condition: Très bon

About this title

Synopsis

Since the dawn of computing, the quest for a better understanding of Nature has been a driving force for technological development. Groundbreaking achievements by great scientists have paved the way from the abacus to the supercomputing power of today. When trying to replicate Nature in the computer’s silicon test tube, there is need for precise and computable process descriptions. The scienti?c ?elds of Ma- ematics and Physics provide a powerful vehicle for such descriptions in terms of Partial Differential Equations (PDEs). Formulated as such equations, physical laws can become subject to computational and analytical studies. In the computational setting, the equations can be discreti ed for ef?cient solution on a computer, leading to valuable tools for simulation of natural and man-made processes. Numerical so- tion of PDE-based mathematical models has been an important research topic over centuries, and will remain so for centuries to come. In the context of computer-based simulations, the quality of the computed results is directly connected to the model’s complexity and the number of data points used for the computations. Therefore, computational scientists tend to ?ll even the largest and most powerful computers they can get access to, either by increasing the si e of the data sets, or by introducing new model terms that make the simulations more realistic, or a combination of both. Today, many important simulation problems can not be solved by one single computer, but calls for parallel computing.