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Optimizing Industrial Design and Operations - Impacts of Uncertainty
Albert Gilg
Coporate Research and Technologies, Siemens AG, Germany
Abstract:
Mathematical optimization is still dominated by deterministic models and corresponding algorithms. But many engineering and industrial optimization challenges demand for more realistic modelling including stochastic effects. Common Monte-Carlo methods are too expensive for engineering applications. Polynomial chaos expansions have found to be an efficient mathematical approach for several industrial applications, like turbomachinery design and production failure reduction.
Tuesday, April 10, 2012
11:00AM AP&M 2402
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Directors:
Randolph E. Bank
Philip E. Gill
Michael Holst
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Albert Gilg
Coporate Research and Technologies, Siemens AG, Germany
Abstract:
Mathematical optimization is still dominated by deterministic models and corresponding algorithms. But many engineering and industrial optimization challenges demand for more realistic modelling including stochastic effects. Common Monte-Carlo methods are too expensive for engineering applications. Polynomial chaos expansions have found to be an efficient mathematical approach for several industrial applications, like turbomachinery design and production failure reduction.
Tuesday, April 10, 2012
11:00AM AP&M 2402