Pengzhan Jin

592 total citations · 1 hit paper
10 papers, 291 citations indexed

About

Pengzhan Jin is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Mathematics. According to data from OpenAlex, Pengzhan Jin has authored 10 papers receiving a total of 291 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Statistical and Nonlinear Physics and 2 papers in Computational Mathematics. Recurrent topics in Pengzhan Jin's work include Neural Networks and Applications (4 papers), Model Reduction and Neural Networks (4 papers) and Tensor decomposition and applications (2 papers). Pengzhan Jin is often cited by papers focused on Neural Networks and Applications (4 papers), Model Reduction and Neural Networks (4 papers) and Tensor decomposition and applications (2 papers). Pengzhan Jin collaborates with scholars based in China and United States. Pengzhan Jin's co-authors include Lu Lu, Shuai Meng, George Em Karniadakis, Yifa Tang, Zhen Zhang, Ioannis G. Kevrekidis and Bo Xiao and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Neural Networks and SIAM Journal on Scientific Computing.

In The Last Decade

Pengzhan Jin

9 papers receiving 285 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.

MIONet: Learning Multiple-Input Operators via Tensor Product

2022 109 citations Pengzhan Jin, Shuai Meng et al. SIAM Journal on Scientific Computing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pengzhan Jin China	7	200	102	51	39	34	10	291
Yiping Lu China	5	260 1.3×	101 1.0×	93 1.8×	26 0.7×	31 0.9×	17	386
Tong Qin United States	7	203 1.0×	68 0.7×	165 3.2×	52 1.3×	39 1.1×	11	387
George Em Karniadakis United States	6	219 1.1×	63 0.6×	126 2.5×	14 0.4×	19 0.6×	14	369
David Sondak United States	9	186 0.9×	61 0.6×	150 2.9×	11 0.3×	19 0.6×	21	321
Chad Lieberman United States	7	126 0.6×	51 0.5×	40 0.8×	44 1.1×	24 0.7×	9	273
Brian M. de Silva United States	5	157 0.8×	64 0.6×	42 0.8×	11 0.3×	78 2.3×	6	264
Kathleen Champion United States	3	221 1.1×	82 0.8×	52 1.0×	13 0.3×	95 2.8×	4	339
Ben Moseley United Kingdom	7	152 0.8×	72 0.7×	68 1.3×	9 0.2×	6 0.2×	13	361
Helin Gong China	11	142 0.7×	32 0.3×	55 1.1×	7 0.2×	60 1.8×	35	388

Countries citing papers authored by Pengzhan Jin

Since Specialization

Citations

This map shows the geographic impact of Pengzhan Jin'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 Pengzhan Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pengzhan Jin more than expected).

Fields of papers citing papers by Pengzhan Jin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pengzhan Jin. 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 Pengzhan Jin. The network helps show where Pengzhan Jin may publish in the future.

Co-authorship network of co-authors of Pengzhan Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Pengzhan Jin. A scholar is included among the top collaborators of Pengzhan Jin 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 Pengzhan Jin. Pengzhan Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Jin, Pengzhan. (2026). Two-hidden-layer ReLU neural networks and finite elements. Neural Networks. 198. 108559–108559.

2.

Jin, Pengzhan, et al.. (2023). Tensor Neural Network and Its Numerical Integration. Journal of Computational Mathematics. 42(6). 1714–1742. 8 indexed citations

3.

Jin, Pengzhan, Zhen Zhang, Ioannis G. Kevrekidis, & George Em Karniadakis. (2022). Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 34(11). 8271–8283. 24 indexed citations

4.

Jin, Pengzhan, Shuai Meng, & Lu Lu. (2022). MIONet: Learning Multiple-Input Operators via Tensor Product. SIAM Journal on Scientific Computing. 44(6). A3490–A3514. 109 indexed citations breakdown →

5.

Jin, Pengzhan, et al.. (2022). Optimal unit triangular factorization of symplectic matrices. Linear Algebra and its Applications. 650. 236–247. 4 indexed citations

6.

Jin, Pengzhan, et al.. (2021). Approximation capabilities of measure-preserving neural networks. Neural Networks. 147. 72–80. 7 indexed citations

7.

Jin, Pengzhan, et al.. (2020). DEEP HAMILTONIAN NEURAL NETWORKS BASED ON SYMPLECTIC INTEGRATORS. 42(3). 370. 1 indexed citations

8.

Jin, Pengzhan, et al.. (2020). SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems. Neural Networks. 132. 166–179. 93 indexed citations

9.

Jin, Pengzhan, et al.. (2020). Unit Triangular Factorization of the Matrix Symplectic Group. SIAM Journal on Matrix Analysis and Applications. 41(4). 1630–1650. 8 indexed citations

10.

Jin, Pengzhan, Lu Lu, Yifa Tang, & George Em Karniadakis. (2019). Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. arXiv (Cornell University). 37 indexed citations

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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