Weinan E

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1 Similar Profiles

8 Finished

Machine Learning via Dynamical Systems
E, W.
Office of Naval Research - MA
9/1/17 → 8/31/21
Project: Research project
Coupling Continuum and Density Functional Theories for Materials Modeling
E, W.
NSF - National Science Foundation
8/15/14 → 1/31/16
Project: Research project
Multi-scale/Multi-physics Modeling: Rare events in Complex Systems
E, W.
Office of Naval Research - MA
2/1/13 → 7/31/16
Project: Research project
Modeling Mesoscale Processes of Scalable Synthesis
E, W.
DOE - Washington
12/1/12 → 11/30/17
Project: Research project
FRG: Collaborative Research: Dynamical Processes in Many-Body Systems: Analysis and Simulations
E, W.
NSF - National Science Foundation
7/1/11 → 6/30/14
Project: Research project

13791 Citations
66 h-Index
211 Article
9 Conference contribution
5 Conference article
4 Paper
More

Generalization error of GAN from the discriminator’s perspective
Yang, H. & Weinan, E., Mar 2022, In: Research in Mathematical Sciences. 9, 1, 8.
Research output: Contribution to journal › Article › peer-review
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
Lu, D., Wang, H., Chen, M., Lin, L., Car, R., E, W., Jia, W. & Zhang, L., Feb 2021, In: Computer Physics Communications. 259, 107624.
Research output: Contribution to journal › Article › peer-review
21 Scopus citations
A deep learning-based ode solver for chemical kinetics
Zhang, T., Zhang, Y., E, W. & Ju, Y., 2021, AIAA Scitech 2021 Forum. American Institute of Aeronautics and Astronautics Inc, AIAA, (AIAA Scitech 2021 Forum).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Scopus citations
Connections between deep learning and partial differential equations
Burger, M., Weinan, E., Ruthotto, L. & Osher, S. J., Jun 2021, In: European Journal of Applied Mathematics. 32, 3, p. 395-396 2 p.
Research output: Contribution to journal › Editorial › peer-review

Open Access
DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
Chen, Y., Zhang, L., Wang, H. & Weinan, E., Jan 12 2021, In: Journal of Chemical Theory and Computation. 17, 1, p. 170-181 12 p.
Research output: Contribution to journal › Article › peer-review
10 Scopus citations

Weinan E

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Machine Learning via Dynamical Systems

Coupling Continuum and Density Functional Theories for Materials Modeling

Multi-scale/Multi-physics Modeling: Rare events in Complex Systems

Modeling Mesoscale Processes of Scalable Synthesis

FRG: Collaborative Research: Dynamical Processes in Many-Body Systems: Analysis and Simulations

Generalization error of GAN from the discriminator’s perspective

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

A deep learning-based ode solver for chemical kinetics

Connections between deep learning and partial differential equations

DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

Weinan E

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