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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Topics in Industrial Mathematics: Case Studies and Related Mathematical Methods

Topics in Industrial Mathematics: Case Studies and Related Mathematical Methods

Siddiqi, Abul Hasan
Neunzert, H.

Hardcover

Series: Applied Optimization, Book 42

Technology & EngineeringGeneral Mathematics

ISBN10: 0792364171
ISBN13: 9780792364177
Publisher: Springer Nature
Published: Oct 31 2000
Pages: 377
Weight: 1.61
Height: 0.88 Width: 6.14 Depth: 9.21
Language: English
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.

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