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科学研究
RESEARCH
[INS COLLOQUIUM] Machine Learning, Adaptive Numerical Approximation and VOF Methods
时间  Datetime
2020-07-07 15:00 — 16:00
地点  Venue
Zoom APP(2)()
报告人  Speaker
Bruno Després
单位  Affiliation
Sorbonne University and IUF, Paris, France
邀请人  Host
INS
备注  remarks
Conference ID: 620-254-80235 PIN Code: 463299
报告摘要  Abstract

Machine learning methods (and more broadly deep learning and artificial intelligence) are giving rise to a new practice of scientific computation.

Their mathematical understanding and their use for the discretization of hyperbolic or parabolic partial differential equations (see for example Hesthaven 2018, Zaleski 2019) require an evaluation or re-evaluation of the foundations of these methods.

A review will be made:
a) a mathematical framework based on adaptive numerical approximation
b) goods and bads about the power of depth assessed from the Takagi function (Yarotsky 2017, Daubechies-DeVore et al. 2019)
c) a recent application to the transport of indicatrix functions with a new numerical scheme VOF-ML.

Bio

Bruno Després is Professor in Applied Mathematics at Sorbonne University. After a PhD thesis on domain decomposition methods at Inria, he started his scientific carrier at CEA. Since then, he is interested by numerous aspects of PDEs and numerical approximation of PDEs with application in physical sciences and plasma physics.

He received the national Blaise Pascal prize in 2002. Recently he was a John Von Neumann Visiting at the Munich Technical University (TUM) in 2019 and visiting scholar at Brown university 2020.