Awarded to Weinan E for his seminal contributions to applied mathematics and in particular on analysis and application of machine learning algorithms, multi-scale modeling, the modeling of rare events and stochastic partial differential equations.
Weinan E is the director of the Center for Machine Learning Research at Peking University and professor in the Department of Mathematics and Program in Applied and Computational Mathematics at Princeton University. He obtained his undergraduate degree at the University of Science and Technology of China in 1982, his master's degree at the Chinese Academy of Sciences in 1985, and his Ph.D. at the University of California, Los Angeles in 1989. Professor E was the recipient of the ICIAM Collatz Prize in 2003, the Peter Henrici Prize of SIAM and ETH in 2019, and the Gordon Bell Prize from ACM in 2020. He has been invited to deliver a plenary lecture at ICM 2022. Professor E was elected fellow of the Institute of Physics in 2005, fellow of SIAM in 2009, member of the Chinese Academy of Sciences in 2011 and fellow of the American Mathematical Society in 2012.
Professor E’s research work draws inspiration from various disciplines of sciences. He has made profound impact in fluid dynamics, chemistry, material sciences, and soft condensed matter physics. He has contributed to the resolution of many long standing scientific problems such as the Burgers turbulence problem, and the Cauchy-Born rule for crystalline solids. A common theme of his work is to bring clarity to scientific issues through mathematics. A second theme is multi-scale and/or multi-physics modeling. He has made fundamental contributions to building the mathematical framework and finding effective numerical algorithms for modeling rare events. He has also made original contributions to multiscale analysis and algorithms through his work on heterogeneous multi-scale methods, multi-scale stochastic simulation algorithms, complex fluids and homogenization problems. In addition, Professor E and collaborators made fundamental contributions to the analysis and numerical algorithms of density functional theory, including studying its continuum limit and developing the PEXSI algorithm. More recently, Professor E pioneered the development of deep learning-based algorithms in scientific computing and computational science. His work on solving high dimensional stochastic control problems using deep learning-based algorithms in 2016 was the first paper on deep learning-based algorithms for high dimensional problems in scientific computing. He and collaborators have developed deep learning-based methodologies in molecular dynamics and quantum mechanics, and he pioneered the dynamical systems and control theory approach to machine learning and maximum principle-based algorithms for deep learning.
Subcommittee for the 2023 ICIAM Maxwell Prize
- Gang Bao (Zhejiang, University China), Chair;
- Wolfgang Dahmen (University of South Carolina, USA);
- Qiang Du (Columbia University, USA);
- Erwan Faou (INRIA and University of Rennes, France);
- Des Higham (University of Edinburgh, UK);
- Amy Novick-Cohen (Technion, Israel).