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Numerical Modeling of Plasmas in Fluid and Kinetic Regimes
Interpretable Quantum Advantage in Neural Sequence Learning
The Awesome Power of Biochemistry in Neuroscience
清华大学材料科学与工程研究院《材料科学论坛》:基于三维微纳结构的仿生光电与传感器...
报告题目:
Why uncertain quantification can be useful in computational electromagnetics?
 报告人:
Prof. Stephane CLENET
教授
报告时间:
2019-06-10 09:50
报告地点:
六教6B105
主办单位:
电机系
  简介:

1. The results given by the numerical model may lead to deviations from “real world” due to input parameters. To account for these uncertain deviations on model parameters, the stochastic approach can be used. An overview on the solution of stochastic problems will be given. 

2. Most of electrical machines are made with FeSi laminations. Currently, Soft Magnetic Composite (SMC) Materials are available to manufacture electrical machine. The comparison of the two materials is made. 


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