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数学系Seminar第1996讲暨上海大学运筹与优化开放实验室系列报告 基于模型的数据同化与数据驱动的机器学习

创建时间:  2020/08/21  龚惠英   浏览次数:   返回

    数学系 Seminar 第1996讲

    暨上海大学运筹与优化开放实验室系列报告

报告主题:Model-based Data Assimilation versus Data-driven Machine Learning(基于模型的数据同化与数据驱动的机器学习)

报告人:Haixiang Ling 教授(荷兰代尔夫特理工大学应用数学系)

报告时间:2020年8月28日(周五) 16:00-18:00

参会方式:腾讯 会议

会议ID:会议ID:876 571 395

会议密码:202028

会议地点:https://meeting.tencent.com/s/Y581PQnWVDxf

主办部门:上海大学运筹与优化开放实验室-国际科研合作平台、上海市运筹学会、十大老品牌网赌网站数学系

报告摘要:Uncertainty is common in real life, both mathematical-physical models and observations contain uncertainties. Data assimilation is a method which uses the information of observation data to reduce the uncertainty in the model consequently improving the forecast accuracy of the model. Machine learning is a data-driven method which tries to find the important features and their relations from the data, in contrast to model-based data assimilation, machine learning techniques do not require a mathematical-physical model and try to fit the data into some functional relationship through an optimization procedure. In this sense machine learning is therefore an “interpolation” method without paying attention to “extrapolation”. Combining the power of the model-based data assimilation method and the data-driven machine learning technique is the focus of many recent research, in this talk we will discuss some examples of this development.

 

欢迎教师、学生参加!

上一条:数学系“60周年”系庆系列报告 在线教育背景下大学数学课程建设与教学改革

下一条:数学系“60周年”系庆系列报告 正交约束优化的新方法


数学系Seminar第1996讲暨上海大学运筹与优化开放实验室系列报告 基于模型的数据同化与数据驱动的机器学习

创建时间:  2020/08/21  龚惠英   浏览次数:   返回

    数学系 Seminar 第1996讲

    暨上海大学运筹与优化开放实验室系列报告

报告主题:Model-based Data Assimilation versus Data-driven Machine Learning(基于模型的数据同化与数据驱动的机器学习)

报告人:Haixiang Ling 教授(荷兰代尔夫特理工大学应用数学系)

报告时间:2020年8月28日(周五) 16:00-18:00

参会方式:腾讯 会议

会议ID:会议ID:876 571 395

会议密码:202028

会议地点:https://meeting.tencent.com/s/Y581PQnWVDxf

主办部门:上海大学运筹与优化开放实验室-国际科研合作平台、上海市运筹学会、十大老品牌网赌网站数学系

报告摘要:Uncertainty is common in real life, both mathematical-physical models and observations contain uncertainties. Data assimilation is a method which uses the information of observation data to reduce the uncertainty in the model consequently improving the forecast accuracy of the model. Machine learning is a data-driven method which tries to find the important features and their relations from the data, in contrast to model-based data assimilation, machine learning techniques do not require a mathematical-physical model and try to fit the data into some functional relationship through an optimization procedure. In this sense machine learning is therefore an “interpolation” method without paying attention to “extrapolation”. Combining the power of the model-based data assimilation method and the data-driven machine learning technique is the focus of many recent research, in this talk we will discuss some examples of this development.

 

欢迎教师、学生参加!

上一条:数学系“60周年”系庆系列报告 在线教育背景下大学数学课程建设与教学改革

下一条:数学系“60周年”系庆系列报告 正交约束优化的新方法