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| Weinan E | |
|---|---|
| Name | Weinan E |
| Native name | 恩维南 |
| Birth date | 1963 |
| Birth place | Nanjing, Jiangsu, China |
| Fields | Mathematics, Computational Science, Physics |
| Alma mater | Peking University, Princeton University |
| Doctoral advisor | Andrew Majda |
| Known for | Multiscale modeling, Molecular dynamics, Numerical analysis, Machine learning for PDEs |
| Awards | MacArthur Fellowship, Sloan Research Fellowship |
Weinan E
Weinan E is a Chinese-American mathematician and computational scientist known for work in multiscale modeling, numerical analysis, and the interface of mathematical physics with data-driven methods. He has held positions at institutions such as Princeton University, Peking University, and Brown University, and has collaborated with researchers connected to Courant Institute of Mathematical Sciences, Los Alamos National Laboratory, and the Institute for Advanced Study. His research spans applications relevant to molecular dynamics, fluid dynamics, and machine learning approaches to partial differential equations.
Born in Nanjing, Jiangsu, E attended schools that prepared him for tertiary study at Peking University where he studied mathematics alongside contemporaries who later joined faculties at Tsinghua University and Fudan University. He pursued graduate study at Princeton University, completing a Ph.D. under the supervision of Andrew Majda with research linking techniques from statistical mechanics to numerical methods. During his doctoral and postdoctoral periods he interacted with scholars from Stanford University, Massachusetts Institute of Technology, and the University of California, Berkeley.
E's academic career includes faculty appointments at Courant Institute of Mathematical Sciences (as a visiting researcher), a professorship at Princeton University, and later a chair at Peking University where he led the BICMR-related initiatives and collaborated with the Beijing International Center for Mathematical Research and the Tsinghua-Berkeley Shenzhen Institute. He has served as director or principal investigator on projects funded by agencies such as the National Science Foundation and research laboratories including the Los Alamos National Laboratory and the Microsoft Research Cambridge lab. E has supervised doctoral students who went on to positions at Columbia University, Harvard University, California Institute of Technology, ETH Zurich, and University of Oxford.
E developed foundational work in multiscale modeling and homogenization theory, connecting asymptotic analysis used in Hamiltonian mechanics with computational algorithms employed in molecular dynamics and kinetic theory. He contributed to the mathematical theory of stochastic differential equations appearing in models related to Langevin dynamics and numerical schemes inspired by symplectic integrators used in long-time simulation of Hamiltonian systems. More recently, E pioneered methods merging deep learning with numerical analysis for solving partial differential equations, influencing efforts at institutions such as Google DeepMind and collaborations with researchers at Carnegie Mellon University and Imperial College London. His work on equation-free modeling and effective dynamics has impacted studies in materials science, climate modeling, and computational studies linked to Los Alamos National Laboratory and the Lawrence Berkeley National Laboratory.
E's awards include a MacArthur Fellowship, a Sloan Research Fellowship, and membership in national academies such as the Chinese Academy of Sciences and election to bodies associated with the American Mathematical Society. He has been an invited speaker at international venues including the International Congress of Mathematicians, the Gordon Research Conferences, and plenary lectures at the SIAM Annual Meeting. E has held named professorships and fellowships at establishments such as Princeton University, Peking University, and visiting positions at the Institute for Advanced Study and Oxford University.
- "Title examples: multiscale modeling and numerical methods" — articles published in journals like Communications on Pure and Applied Mathematics, Journal of Computational Physics, and SIAM Journal on Numerical Analysis, often coauthored with collaborators from Stanford University and Princeton University. - Works on stochastic modeling and molecular dynamics appearing alongside papers in Physical Review Letters and proceedings linked to International Conference on Machine Learning and NeurIPS workshops. - Monographs and surveys on homogenization, numerical analysis, and machine learning for PDEs used in graduate courses at Massachusetts Institute of Technology and Peking University.
E's mentorship influenced a generation of researchers who now hold positions at institutions such as Columbia University, Harvard University, ETH Zurich, University of Cambridge, and University of Chicago. His blend of rigorous analysis and computational innovation continues to shape interdisciplinary programs bridging departments at Peking University, Princeton University, and international centers like the Simons Foundation-supported initiatives. He is associated with efforts to promote collaboration between Chinese and Western research communities through exchanges involving Tsinghua University and the University of California system.
Category:Chinese mathematicians Category:Computational scientists