Place: Beijing Computational Science Research Center (CSRC)
Objective: To provide a systematic course for researchers on the latest development of stochastic computational methods and uncertainty quantification (UQ) for complex network, data science, and Bayesian estimation.
讲授教师与授课内容
Prof. George Karniadakis, Applied Mathematics, Brown University
Bayesian estimate, machine learning, multi-fidelity models 贝叶斯估计，机器学习，多保真模型
Prof. Tiejun Li, 北京大学数学学院
Modularity structure of complex networks, Rare events for chemical reaction networks
复杂网络的模块化结构，化学反应网络稀有事件
Prof. Yaohang Li, Computer Science, Old Dominion University
Monte Carlo methods for numerical linear algebra problems and algorithms for big data
数值线性代数中的蒙特卡罗方法和大数据问题
Prof. Tao Zhou,中科院数学与系统科学研究院
Basics and recent development of Uncertainty quantification (UQ) and applications
不确定性量化 (UQ)的方法与最新进展及其应用
Course website and online registration:http://www.csrc.ac.cn/schools/StochasticsUQ
Contact: 范颖, fanying@csrc.ac.cn, tel. 86-10-56981715
}, issn = {}, doi = {https://doi.org/2017-CAM-14390}, url = {https://global-sci.com/article/76147/workshop-on-stochastic-computing-and-uncertainty-quantification-uq-beijing-june-19-22-2017} }