| 제 목 | Application of stochastic methods in engineering and beyond | ||||
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| 작성자 | 백현애 | 작성일 | 2007-08-10 | 조회수 | 818 |
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1. 제 목 : Application of stochastic methods in engineering and beyond
2. 발 표 자 : Doo-Bo Chung(Risk Analyst, ABN Amro Bank, 네델란드)
3. 일 시 : 2007년 8월 10일(금) 14:00~16:00
4. 장 소 : 경북대 공대11호관 103호
5. 초청교수 : 공 성 호 교수
6. 강사약력
2007 : Risk Analyst, ABN Amro Bank, Amsterdam, the Netherlands
2007 : PhD Aerospace Engineering, Delft University of Technology, the Netherlands
2002 : MSc Aerospace Engineering, Delft University of Technology (cum laude)
1996 : Member Suriname National Delegation to the International Physics Olympiade, Oslo, Norway
1996 : Winner Surinamese National Physics Olympiade
1996 : High school Vrije Atheneum, Paramaribo, Suriname
7. 내용요약
We live in a world surrounded in uncertainty. Realistic engineering systems face uncertainties throughout their lifecycle. For example the operating environment and human inputs to a system are uncertain by nature.
However engineers in practice still rely on deterministic methods to describe an inherently uncertain world. Traditionally engineers have circumvented this obvious flaw in their design methodology by introducing safety factors in calculations, leading to inefficient designs and worse no insight into the actual existing risks.
Non-deterministic methods provide the analytical framework to deal with these uncertainties. The main drawback to these methods is their computational inefficiency and availability of data. Monte Carlo simulation, which is a traditional method to evaluate uncertainties, requires large sets of realizations. In most engineering designs the computational costs of running countless simulations is still prohibitive and not practical.
The extension of deterministic methods to capture uncertainties is a shift in paradigm. The extensions of deterministic methods such as finite element methods to stochastic finite element methods combine the numerical efficiency of existing techniqueswith the sophistication of traditional non-deterministic techniques. The use of these techniques allowsengineers to better capture the risk inherent in the system and rationalize the design and decision process.
This presentation deals with application of stochastic methods in engineering and its implication on risk management of complex engineering systems. The treatment and acquisition of data in different settings are discussed, as well as characterization in terms of statistics. Examples in the presentation also deal with the application of the stochastic paradigm to fields beyond engineering, specifically risk management and analysis within a business setting.
※ 주최 : BK21 정보기술연구인력양성사업단, 디스플레이기술교육센터(DTEC),
반도체공정교육 및 지원센터(NECST)
■ 문의처 : 반도체공정교육 및 지원센터(NECST) ☎ 950-7591 ■ |
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