全校師生：
我校定于2017年5月16日舉辦研究生靈犀學(xué)術(shù)殿堂——Dr Sergei Kucherenko報(bào)告會(huì)，現(xiàn)將有關(guān)事項(xiàng)通知如下：
1.報(bào)告會(huì)簡(jiǎn)介
報(bào)告人：Sergei Kucherenko研究員
時(shí)間：2017年5月16日（星期二）上午9：30
地點(diǎn)：友誼校區(qū)航空樓 A706
主題：Global sensitivity analysis, metamodeling and optimization algorithm
內(nèi)容簡(jiǎn)介：Sensitivity can reflect the effect of input variables on output response. Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol’ method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. Many simulation methods can calculate the Sobol’ sensitivity indices, such as the Monte Carlo (MC) method or the quasi-MC (QMC) method. High dimensional model representation (HDMR) is a popular way to compute Sobol’ indices. Derivative-based global sensitivity measure (DGSM) is proposed and discovered the link between DGSM and the total sensitivity index.
In the cases of computationally expensive models the metamodelling technique which maps inputs and outputs is a very useful and practical way of making computations tractable. A number of new techniques which improve the efficiency of the Random Sampling-High dimensional model representation (RS-HDMR) for models with independent and dependent input variables are presented. Two different metamodelling methods for models with dependent input variables are compared. Both techniques are based on Quasi Monte Carlo variant of RS-HDMR. The first technique makes use of transformation of the dependent input vector into a Gaussian independent random vector and then applying decomposition of the model using a tensored Hermite polynomial basis. The second approach uses a direct decomposition of the model function into a basis which consists of the marginal distributions of input components and their joint distribution.
2.歡迎各學(xué)院師生前來(lái)聽報(bào)告。報(bào)告會(huì)期間請(qǐng)關(guān)閉手機(jī)或?qū)⑹謾C(jī)調(diào)至靜音模式。
黨委研究生工作部
航空學(xué)院
2017年5月15日
報(bào)告人簡(jiǎn)介
Dr Sergei Kucherenkois the research fellow at Imperial College since 2000, and is the member ofEuropean Commission's Science Service.He obtained his PhD degree fromNational Research Nuclear University (former Moscow Engineering Physics Institute) in 1984. He has published over 20 journal papers in Reliability Engineering and System Safety, Mathematics and Computers in Simulation, Computer Physics Communications and so on.Sergei is very reliable and efficient researcher. His major is numerical algorithms and mathematics.
