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＜電子ブック＞
Topics in Industrial Mathematics : Case Studies and Related Mathematical Methods / by H Neunzert, Abul Hasan Siddiqi
(Applied Optimization ; 42)

版	1st ed. 2000.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	2000
本文言語	英語
大きさ	XIII, 377 p : online resource
著者標目	*Neunzert, H author Siddiqi, Abul Hasan author SpringerLink (Online service)
件　名	LCSH:Mathematical models LCSH:Algorithms LCSH:Mathematical optimization LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Numerical analysis FREE:Mathematical Modeling and Industrial Mathematics FREE:Algorithms FREE:Optimization FREE:Computational Mathematics and Numerical Analysis FREE:Numerical Analysis
一般注記	1 Case Studies at Kaiserslautern -- 2 Algorithms for Optimization -- 3 Maxwell’s Equations and Numerical Methods -- 4 Monte Carlo Methods -- 5 Image Processing -- 6 Models of Hysteresis and Applications -- 7 Appendix -- Symbols Industrial Mathematics is a relatively recent discipline. It is concerned primarily with transforming technical, organizational and economic problems posed by indus­ try into mathematical problems; "solving" these problems byapproximative methods of analytical and/or numerical nature; and finally reinterpreting the results in terms of the original problems. In short, industrial mathematics is modelling and scientific computing of industrial problems. Industrial mathematicians are bridge-builders: they build bridges from the field of mathematics to the practical world; to do that they need to know about both sides, the problems from the companies and ideas and methods from mathematics. As mathematicians, they have to be generalists. If you enter the world of indus­ try, you never know which kind of problems you will encounter, and which kind of mathematical concepts and methods you will need to solve them. Hence, to be a good "industrial mathematician" you need to know a good deal of mathematics as well as ideas already common in engineering and modern mathematics with tremen­ dous potential for application. Mathematical concepts like wavelets, pseudorandom numbers, inverse problems, multigrid etc., introduced during the last 20 years have recently started entering the world of real applications. Industrial mathematics consists of modelling, discretization, analysis and visu­ alization. To make a good model, to transform the industrial problem into a math­ ematical one such that you can trust the prediction of the model is no easy task HTTP:URL=https://doi.org/10.1007/978-1-4757-3222-1

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