Jiequn Han

9.2k total citations · 4 hit papers
44 papers, 5.1k citations indexed

About

Jiequn Han is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Materials Chemistry. According to data from OpenAlex, Jiequn Han has authored 44 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistical and Nonlinear Physics, 13 papers in Artificial Intelligence and 9 papers in Materials Chemistry. Recurrent topics in Jiequn Han's work include Model Reduction and Neural Networks (15 papers), Machine Learning in Materials Science (9 papers) and Stochastic processes and financial applications (6 papers). Jiequn Han is often cited by papers focused on Model Reduction and Neural Networks (15 papers), Machine Learning in Materials Science (9 papers) and Stochastic processes and financial applications (6 papers). Jiequn Han collaborates with scholars based in United States, China and Germany. Jiequn Han's co-authors include E Weinan, Linfeng Zhang, Arnulf Jentzen, Han Wang, Handong Wang, Roberto Car, Benjamin Moll, Yves Achdou, Jianfeng Lu and Pierre-Louis Lions and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Physics Today.

In The Last Decade

Jiequn Han

40 papers receiving 5.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

2018 1.4k citations Linfeng Zhang, Jiequn Han et al. profile →
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

2018 1.4k citations Han Wang, Linfeng Zhang et al. Computer Physics Communications profile →
Solving high-dimensional partial differential equations using deep learning

2018 996 citations Jiequn Han, Arnulf Jentzen et al. profile →
Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations

2017 456 citations E Weinan, Jiequn Han et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jiequn Han United States	17	2.4k	1.4k	740	616	612	44	5.1k
Michael Griebel Germany	41	1.2k 0.5×	505 0.4×	559 0.8×	475 0.8×	2.3k 3.8×	148	6.7k
Chao Yang United States	35	1.5k 0.6×	353 0.3×	972 1.3×	2.1k 3.4×	809 1.3×	168	6.3k
Félix Otto Germany	36	1.1k 0.4×	572 0.4×	421 0.6×	510 0.8×	1.5k 2.4×	183	6.1k
Robert M. Ziff United States	52	2.0k 0.8×	2.3k 1.7×	334 0.5×	1.2k 2.0×	398 0.7×	173	9.3k
Hans Christian Öttinger Switzerland	38	2.5k 1.0×	1.8k 1.3×	313 0.4×	723 1.2×	2.0k 3.2×	207	7.8k
Russel E. Caflisch United States	41	829 0.3×	698 0.5×	368 0.5×	566 0.9×	2.4k 3.9×	139	6.5k
Wolfgang Paul Germany	57	5.0k 2.1×	617 0.5×	628 0.8×	3.0k 4.8×	430 0.7×	247	10.8k
Ronald F. Boisvert United States	24	413 0.2×	661 0.5×	625 0.8×	972 1.6×	528 0.9×	68	4.7k
Weiqing Ren United States	25	1.1k 0.5×	647 0.5×	335 0.5×	998 1.6×	1.1k 1.9×	61	4.4k
Chun Liu China	41	1.7k 0.7×	454 0.3×	470 0.6×	469 0.8×	1.9k 3.2×	214	6.2k

Countries citing papers authored by Jiequn Han

Since Specialization

Citations

This map shows the geographic impact of Jiequn Han's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jiequn Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiequn Han more than expected).

Fields of papers citing papers by Jiequn Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jiequn Han. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jiequn Han. The network helps show where Jiequn Han may publish in the future.

Co-authorship network of co-authors of Jiequn Han

This figure shows the co-authorship network connecting the top 25 collaborators of Jiequn Han. A scholar is included among the top collaborators of Jiequn Han based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jiequn Han. Jiequn Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Hu, Wei, et al.. (2025). Solving optimal control problems of rigid-body dynamics with collisions using the hybrid minimum principle. Communications in Nonlinear Science and Numerical Simulation. 143. 108603–108603.

Han, Jiequn, et al.. (2025). Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulations. Journal of Computational Physics. 529. 113871–113871.

Han, Jiequn, et al.. (2024). Offline supervised learning v.s. online direct policy optimization: A comparative study and a unified training paradigm for neural network-based optimal feedback control. Physica D Nonlinear Phenomena. 462. 134130–134130. 5 indexed citations

Bruna, Joan & Jiequn Han. (2024). Provable Posterior Sampling with Denoising Oracles via Tilted Transport. 82863–82894.

Guo, Jin, Ting Gao, Peng Zhang, Jiequn Han, & Jinqiao Duan. (2023). Deep reinforcement learning in finite-horizon to explore the most probable transition pathway. Physica D Nonlinear Phenomena. 458. 133955–133955. 3 indexed citations

Han, Jiequn, et al.. (2023). A class of dimension-free metrics for the convergence of empirical measures. Stochastic Processes and their Applications. 164. 242–287. 1 indexed citations

Han, Jiequn, et al.. (2023). An equivariant neural operator for developing nonlocal tensorial constitutive models. Journal of Computational Physics. 488. 112243–112243. 6 indexed citations

Zhou, Mo, Jiequn Han, Manas Rachh, & Carlos F. Borges. (2023). A neural network warm-start approach for the inverse acoustic obstacle scattering problem. Journal of Computational Physics. 490. 112341–112341. 7 indexed citations

Han, Jiequn, et al.. (2022). An Equivariant Neural Operator for Developing Nonlocal Tensorial Constitutive Models. SSRN Electronic Journal. 1 indexed citations

10.

Han, Jiequn, et al.. (2022). Convergence of deep fictitious play for stochastic differential games. 1(2). 287–287. 9 indexed citations

11.

Weinan, E, et al.. (2022). Empowering Optimal Control with Machine Learning: A Perspective from Model Predictive Control. IFAC-PapersOnLine. 55(30). 121–126. 3 indexed citations

12.

Han, Jiequn, et al.. (2022). Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space. 1(1). 1–37. 2 indexed citations

13.

Yao, Xuan, et al.. (2022). Pandemic Control, Game Theory, and Machine Learning. Notices of the American Mathematical Society. 69(11). 1–1. 2 indexed citations

14.

Weinan, E, Jiequn Han, & Linfeng Zhang. (2021). Machine-learning-assisted modeling. Physics Today. 74(7). 36–41. 18 indexed citations

15.

Weinan, E, Jiequn Han, & Arnulf Jentzen. (2021). Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning. Nonlinearity. 35(1). 278–310. 94 indexed citations

16.

Guo, Xin, et al.. (2020). Perturbed gradient descent with occupation time.. arXiv (Cornell University).

17.

Weinan, E, Jiequn Han, & Qianxiao Li. (2019). A Mean-Field Optimal Control Formulation of Deep Learning. National University of Singapore. 1 indexed citations

18.

Han, Jiequn, et al.. (2019). Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games. RePEc: Research Papers in Economics. 221–245. 4 indexed citations

19.

Zhang, Linfeng, Jiequn Han, Handong Wang, et al.. (2018). End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. neural information processing systems. 31. 4436–4446. 129 indexed citations

20.

Wang, Han, Linfeng Zhang, Jiequn Han, & E Weinan. (2018). DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Computer Physics Communications. 228. 178–184. 1352 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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