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Distinguished Lecture in Celebration of the 10th Anniversary of INS——机器学习与计算数学

1553ebe04f95ee1cceec74f78343e4ed915ea476

Speaker

Weinan E, Princeton University, USA

Time

2020.10.02 15:00-16:00

Venue

Room 300, No. 5&6 Science Building, Minhang Campus, Shanghai Jiao Tong University

Abstract

The heart of machine learning is the approximation of functions using finite pieces of data. This is one of the main pillars of computational mathematics. Thus it is not surprising that the success of machine learning in dealing with functions in very high dimensions has opened up some brand new territories in computational mathematics, with potentially unprecedented impact for years to come.

In the first part of this talk, I will review some of the most exciting advances of using machine learning to address problems in scientific computing and computational science.

In the second part of this talk, I will discuss how machine learning can be formulated as a problem in computational mathematics and how ideas from numerical analysis can be used to understand machine learning as well as construct new machine learning models and algorithms.

Bio

Weinan E, mathematician, is mainly engaged in the research of machine learning, computational mathematics, applied mathematics and their applications in the fields of mechanics, physics, chemistry and engineering. In 1999, he became a professor of the Department of Mathematics and Applied Mathematics and Computational Mathematics of Princeton University. In 2011, he was elected as an academician of the Chinese Academy of Sciences. In 2012, he became a fellow of the American Mathematical Society.

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