Weinan E
Orcid: 0000-0003-0272-9500Affiliations:
- Beijing Institute of Big Data Research, China
- Princeton University, Department of Mathematics, NJ, USA
- Peking University, China
According to our database1,
Weinan E
authored at least 108 papers
between 2000 and 2024.
Collaborative distances:
Collaborative distances:
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Online presence:
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on orcid.org
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on id.loc.gov
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Bibliography
2024
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling.
CoRR, 2024
CoRR, 2024
2023
DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models.
Comput. Phys. Commun., 2023
CoRR, 2023
CoRR, 2023
An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
2022
IEEE Trans. Pattern Anal. Mach. Intell., 2022
Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics.
Nat. Comput. Sci., 2022
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks.
J. Mach. Learn. Res., 2022
Bridging Traditional and Machine Learning-based Algorithms for Solving PDEs: The Random Feature Method.
CoRR, 2022
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics.
CoRR, 2022
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics.
CoRR, 2022
Proceedings of the Mathematical and Scientific Machine Learning, 2022
2021
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with <i>ab initio</i> accuracy.
Comput. Phys. Commun., 2021
CoRR, 2021
DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks.
CoRR, 2021
An L<sup>2</sup> Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation.
CoRR, 2021
Proceedings of the Mathematical and Scientific Machine Learning, 2021
On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers.
Proceedings of the Mathematical and Scientific Machine Learning, 2021
Some observations on high-dimensional partial differential equations with Barron data.
Proceedings of the Mathematical and Scientific Machine Learning, 2021
Proceedings of the Mathematical and Scientific Machine Learning, 2021
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis.
Proceedings of the 9th International Conference on Learning Representations, 2021
2020
Can Shallow Neural Networks Beat the Curse of Dimensionality? A Mean Field Training Perspective.
IEEE Trans. Artif. Intell., 2020
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models.
Comput. Phys. Commun., 2020
On the emergence of tetrahedral symmetry in the final and penultimate layers of neural network classifiers.
CoRR, 2020
Some observations on partial differential equations in Barron and multi-layer spaces.
CoRR, 2020
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't.
CoRR, 2020
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle.
CoRR, 2020
Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning.
CoRR, 2020
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory.
CoRR, 2020
On the Banach spaces associated with multi-layer ReLU networks: Function representation, approximation theory and gradient descent dynamics.
CoRR, 2020
Coarse-grained spectral projection (CGSP): A scalable and parallelizable deep learning-based approach to quantum unitary dynamics.
CoRR, 2020
The Quenching-Activation Behavior of the Gradient Descent Dynamics for Two-layer Neural Network Models.
CoRR, 2020
Kolmogorov Width Decay and Poor Approximators in Machine Learning: Shallow Neural Networks, Random Feature Models and Neural Tangent Kernels.
CoRR, 2020
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy.
CoRR, 2020
Pushing the limit of molecular dynamics with <i>ab initio</i> accuracy to 100 million atoms with machine learning.
Proceedings of the International Conference for High Performance Computing, 2020
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of Mathematical and Scientific Machine Learning, 2020
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
2019
On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations.
J. Sci. Comput., 2019
Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations.
J. Nonlinear Sci., 2019
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations.
J. Mach. Learn. Res., 2019
J. Comput. Phys., 2019
On the Generalization Properties of Minimum-norm Solutions for Over-parameterized Neural Network Models.
CoRR, 2019
CoRR, 2019
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections.
CoRR, 2019
A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics.
CoRR, 2019
2018
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.
Comput. Phys. Commun., 2018
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation.
CoRR, 2018
CoRR, 2018
CoRR, 2018
Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions.
CoRR, 2018
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic Methodology.
CoRR, 2017
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems.
CoRR, 2017
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics.
CoRR, 2017
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes.
CoRR, 2017
Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning.
CoRR, 2017
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations.
CoRR, 2017
Joint Learning of Response Ranking and Next Utterance Suggestion in Human-Computer Conversation System.
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017
Proceedings of the 34th International Conference on Machine Learning, 2017
2016
J. Mach. Learn. Res., 2016
Proceedings of the 4th International Conference on Learning Representations, 2016
Proceedings of the 55th IEEE Conference on Decision and Control, 2016
2015
2014
Proceedings of the 48th Annual Conference on Information Sciences and Systems, 2014
2013
Efficient iterative method for solving the Dirac-Kohn-Sham density functional theory.
J. Comput. Phys., 2013
2012
J. Comput. Phys., 2012
Adaptive local basis set for Kohn-Sham density functional theory in a discontinuous Galerkin framework I: Total energy calculation.
J. Comput. Phys., 2012
2011
ACM Trans. Math. Softw., 2011
A Fast Parallel Algorithm for Selected Inversion of Structured Sparse Matrices with Application to 2D Electronic Structure Calculations.
SIAM J. Sci. Comput., 2011
Proceedings of the Winter Simulation Conference 2011, 2011
2010
J. Comput. Phys., 2010
A multiscale coupling method for the modeling of dynamics of solids with application to brittle cracks.
J. Comput. Phys., 2010
2009
2007
The local microscale problem in the multiscale modeling of strongly heterogeneous media: Effects of boundary conditions and cell size.
J. Comput. Phys., 2007
Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales.
J. Comput. Phys., 2007
A discontinuous Galerkin implementation of a domain decomposition method for kinetic-hydrodynamic coupling multiscale problems in gas dynamics and device simulations.
J. Comput. Phys., 2007
2005
The Heterogeneous Multiscale Method Based on the Discontinuous Galerkin Method for Hyperbolic and Parabolic Problems.
Multiscale Model. Simul., 2005
2002
Math. Comput., 2002
2001
Math. Comput., 2001
2000