Weinan E
Weinan E
Professor of Mathematics, Princeton University
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Cited by
Cited by
Solving high-dimensional partial differential equations using deep learning
J Han, A Jentzen, W E
Proceedings of the National Academy of Sciences 115 (34), 8505-8510, 2018
Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
L Zhang, J Han, H Wang, R Car, W E
Physical review letters 120 (14), 143001, 2018
String method for the study of rare events
E Weinan, W Ren, E Vanden-Eijnden
Physical Review B 66 (5), 052301, 2002
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
H Wang, L Zhang, J Han, E Weinan
Computer Physics Communications 228, 178-184, 2018
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
J Han, A Jentzen
Communications in mathematics and statistics 5 (4), 349-380, 2017
A proposal on machine learning via dynamical systems
E Weinan
Communications in Mathematics and Statistics 1 (5), 1-11, 2017
Onsager's conjecture on the energy conservation for solutions of Euler's equation
P Constantin, W E, ES Titi
The heterognous multiscale methods
E Weinan, B Engquist
Communications in Mathematical Sciences 1 (1), 87-132, 2003
Heterogeneous multiscale methods: a review
E Weinan, B Engquist, X Li, W Ren, E Vanden-Eijnden
Communications in computational physics 2 (3), 367-450, 2007
Principles of multiscale modeling
E Weinan
Cambridge University Press, 2011
Transition-path theory and path-finding algorithms for the study of rare events
E Weinan, E Vanden-Eijnden
Annual review of physical chemistry 61 (2010), 391-420, 2010
The heterogeneous multiscale method
A Abdulle, E Weinan, B Engquist, E Vanden-Eijnden
Acta Numerica 21, 1-87, 2012
Active learning of uniformly accurate interatomic potentials for materials simulation
L Zhang, DY Lin, H Wang, R Car, W E
Physical Review Materials 3 (2), 023804, 2019
Finite temperature string method for the study of rare events
E Weinan, W Ren, E Vanden-Eijnden
J. Phys. Chem. B 109 (14), 6688-6693, 2005
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Y Zhang, H Wang, W Chen, J Zeng, L Zhang, H Wang, E Weinan
Computer Physics Communications 253, 107206, 2020
Towards a theory of transition paths
E Vanden-Eijnden
Journal of statistical physics 123 (3), 503-523, 2006
Heterogeneous multiscale method: a general methodology for multiscale modeling
E Weinan, B Engquist, Z Huang
Physical Review B 67 (9), 092101, 2003
Stochastic modified equations and adaptive stochastic gradient algorithms
Q Li, C Tai, E Weinan
International Conference on Machine Learning, 2101-2110, 2017
Boundary conditions for the moving contact line problem
W Ren
Physics of fluids 19 (2), 2007
Invariant measures for Burgers equation with stochastic forcing
E Weinan, K Khanin, A Mazel, Y Sinai
Annals of mathematics, 877-960, 2000
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