报告题目（Title）：Selected findings in Stochastic Numerics and Theoretical Machine Learning: from the Training of Neural Networks to the Approximation of Stochastic Differential Equations

报告摘要（Abstract）：In this work we review a few selected recent findings in stochastic numerics and theoretical machine learning. In particular, the talk covers material on numerical approximations for stochastic differential equations (SDEs) with non-globally Lipschitz continuous nonlinearities, on weak convergence rates for stochastic partial differential equations (SPDEs), as well as on the analysis of deep learning approximations for partial differential equations (PDEs).

报告人（Speaker）：Prof. Dr. Arnulf Jentzen[1,2]

[1] School of Data Science and Shenzhen Research Institut of Big Data, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China

[2] Applied Mathematics: Institute of Analysis and Numerics, Faculty of Mathematics and Computer Science, University of Münster, Germany

报告时间（Time）：2024. 6. 14  11:00 - 12:00

报告地点（Location）：数理楼245教室（Classroom 245）

报告人简介（Brief bio）：Arnulf Jentzen (*November 1983) is appointed as a presidential chair professor at the Chinese University of Hong Kong, Shenzhen (since 2021) and as a full professor at the University of Münster (since 2019). In 2004 he started his undergraduate studies in mathematics at Goethe University Frankfurt in Germany, in 2007 he received his diploma degree at this university, and in 2009 he completed his PhD in mathematics at this university. The core research topics of his research group are machine learning approximation algorithms, computational stochastics, numerical analysis for high dimensional partial differential equations (PDEs), stochastic analysis, and computational finance. His research works were published on top journals such as Proc. Natl. Acad. Sci., Mem. Amer. Math. Soc., Trans. Amer. Math. Soc., Ann. Probab., Ann. Appl. Probab., SIAM J. Numer. Anal., Math. Comp., Math. Finance. Currently, he serves in the editorial boards of several scientific journals such as Annals of Applied Probability, SIAM Journal on Numerical Analysis, SIAM Journal on Scientific Computing, SIAM/ASA Journal on Uncertainty Quantification, Communications in Mathematical Sciences, Journal of Machine Learning and Journal of Mathematical Analysis and Applications. In 2020 he was the recipient of the Felix Klein Prize of the European Mathematical Society (EMS), in 2022 he has been awarded an ERC Consolidator Grant from the European Research Council (ERC), and in 2022 he has been awarded the Joseph F. Traub Prize for Achievement in Information-Based Complexity. Further details on the activities of his research group can be found at the webpage http://www.ajentzen.de.