Jiequn Han

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Linfeng ZhangDP Technology; AI for Science InstituteVerifierad e-postadress på dp.tech
Weinan EProfessor of Mathematics, Princeton UniversityVerifierad e-postadress på math.princeton.edu
Roberto CarRalph W Dornte Professor in Chemistry, Princeton University Verifierad e-postadress på princeton.edu
Jihao LongResearcher, Institue for Adavanced Algorithm Research, ShanghaiVerifierad e-postadress på iaar.ac.cn
Arnulf JentzenThe Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) & University of MünsterVerifierad e-postadress på uni-muenster.de
Ruimeng HuAssociate Professor, University of California, Santa BarbaraVerifierad e-postadress på ucsb.edu
Qianxiao LiAssistant Professor, Department of Mathematics and Institute for Functional Intelligent MaterialsVerifierad e-postadress på nus.edu.sg
Benjamin MollSir John Hicks Professor of Economics, London School of EconomicsVerifierad e-postadress på lse.ac.uk
Jianfeng Lu 鲁剑锋Department of Mathematics, Department of Chemistry and Department of Physics, Duke UniversityVerifierad e-postadress på math.duke.edu
Heng XiaoProfessor, University of StuttgartVerifierad e-postadress på simtech.uni-stuttgart.de
Han Wang (王涵)Institute of Applied Physics and Computational MathematicsVerifierad e-postadress på iapcm.ac.cn
Joan BrunaProfessor of Computer Science, Data Science & Mathematics (aff), Courant Institute and CDS, NYUVerifierad e-postadress på cims.nyu.edu
Lin LinUniversity of California, BerkeleyVerifierad e-postadress på math.berkeley.edu
Brandon AmosMetaVerifierad e-postadress på fb.com
Ricky Tian Qi ChenMeta FAIRVerifierad e-postadress på meta.com
Carles Domingo-EnrichMicrosoft ResearchVerifierad e-postadress på nyu.edu

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Jiequn Han

Flatiron Institute, Simons Foundation

Verifierad e-postadress på flatironinstitute.org - Startsida

Applied Mathematics Machine Learning


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	2095	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	2022	2018
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics H Wang, L Zhang, J Han, W E Computer Physics Communications 228, 178-184, 2018	1416	2018
Income and wealth distribution in macroeconomics: A continuous-time approach Y Achdou, J Han, JM Lasry, PL Lions, B Moll The review of economic studies 89 (1), 45-86, 2022	819*	2022
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations W E, J Han, A Jentzen Communications in Mathematics and Statistics 5 (4), 349-380, 2017	694*	2017
End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems L Zhang, J Han, H Wang, W Saidi, R Car, W E Advances in Neural Information Processing Systems, 4436-4446, 2018	587	2018
Deep potential: a general representation of a many-body potential energy surface J Han, L Zhang, R Car, W E Communications in Computational Physics 23 (3), 629-639, 2018	281	2018
Deep learning approximation for stochastic control problems J Han, W E Advances in Neural Information Processing Systems, Deep Reinforcement …, 2016	240*	2016
DeePMD-kit v2: A software package for deep potential models J Zeng, D Zhang, D Lu, P Mo, Z Li, Y Chen, M Rynik, L Huang, Z Li, S Shi, ... The Journal of Chemical Physics 159 (5), 2023	235	2023
Solving many-electron Schrödinger equation using deep neural networks J Han, L Zhang, W E Journal of Computational Physics 399, 108929, 2019	231	2019
DeePCG: Constructing coarse-grained models via deep neural networks L Zhang, J Han, H Wang, R Car The Journal of chemical physics 149 (3), 2018	211	2018
Convergence of the deep BSDE method for coupled FBSDEs J Han, J Long Probability, Uncertainty and Quantitative Risk 5 (1), 5, 2020	185	2020
Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning W E, J Han, A Jentzen Nonlinearity 35 278, 2021	184*	2021
A mean-field optimal control formulation of deep learning W E, J Han, Q Li Research in the Mathematical Sciences 6 (1), 10, 2019	168*	2019
Uniformly accurate machine learning-based hydrodynamic models for kinetic equations J Han, C Ma, Z Ma, W E Proceedings of the National Academy of Sciences 116 (44), 21983-21991, 2019	97	2019
Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach J Han, J Lu, M Zhou Journal of Computational Physics 423, 109792, 2020	91	2020
From microscopic theory to macroscopic theory: a systematic study on static modeling for liquid crystals J Han, Y Luo, W Wang, P Zhang Archive for Rational Mechanics and Analysis 215 (3), 741–809, 2013	91*	2013
Dynamical Systems and Optimal Control Approach to Deep Learning W E, J Han, Q Li Mathematical Aspects of Deep Learning, 422, 2022	80*	2022
Neural-network quantum states for periodic systems in continuous space G Pescia, J Han, A Lovato, J Lu, G Carleo Physical Review Research 4 (2), 023138, 2022	63	2022
Actor-critic method for high dimensional static Hamilton--Jacobi--Bellman partial differential equations based on neural networks M Zhou, J Han, J Lu SIAM Journal on Scientific Computing 43 (6), A4043-A4066, 2021	62	2021

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