Yulong Lu

650 total citations
31 papers, 237 citations indexed

About

Yulong Lu is a scholar working on Statistical and Nonlinear Physics, Mathematical Physics and Statistics and Probability. According to data from OpenAlex, Yulong Lu has authored 31 papers receiving a total of 237 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Statistical and Nonlinear Physics, 7 papers in Mathematical Physics and 7 papers in Statistics and Probability. Recurrent topics in Yulong Lu's work include Model Reduction and Neural Networks (7 papers), Markov Chains and Monte Carlo Methods (7 papers) and Statistical Mechanics and Entropy (4 papers). Yulong Lu is often cited by papers focused on Model Reduction and Neural Networks (7 papers), Markov Chains and Monte Carlo Methods (7 papers) and Statistical Mechanics and Entropy (4 papers). Yulong Lu collaborates with scholars based in United States, China and United Kingdom. Yulong Lu's co-authors include Jianfeng Lu, James Nolen, Hendrik Weber, Jonathan C. Mattingly, Li Wang, Andrew M. Stuart, Guanghui Hu, Bo Zhang, Xiangxiong Zhang and Yu Cao and has published in prestigious journals such as Journal of Computational Physics, Mathematics of Computation and Neural Networks.

In The Last Decade

Yulong Lu

31 papers receiving 227 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yulong Lu United States	10	81	63	50	42	27	31	237
Sui Tang United States	9	84 1.0×	63 1.0×	18 0.4×	36 0.9×	29 1.1×	28	255
Nicola Bruti‐Liberati Australia	6	46 0.6×	33 0.5×	32 0.6×	34 0.8×	24 0.9×	10	352
Karl Friedrich Siburg Germany	11	92 1.1×	36 0.6×	95 1.9×	129 3.1×	8 0.3×	34	396
Sylvain Maire France	10	33 0.4×	18 0.3×	26 0.5×	26 0.6×	36 1.3×	28	251
Franca Hoffmann United States	8	32 0.4×	51 0.8×	28 0.6×	42 1.0×	15 0.6×	18	261
Anatoliy Malyarenko Sweden	10	49 0.6×	18 0.3×	9 0.2×	33 0.8×	13 0.5×	46	292
Ken Kuriyama Japan	9	168 2.1×	96 1.5×	41 0.8×	54 1.3×	7 0.3×	33	400
Samuel Taylor United States	6	51 0.6×	19 0.3×	53 1.1×	189 4.5×	6 0.2×	10	431
Daniel Rudolf Germany	10	14 0.2×	58 0.9×	110 2.2×	24 0.6×	13 0.5×	41	279
Peter Becker–Kern Germany	7	57 0.7×	9 0.1×	33 0.7×	104 2.5×	5 0.2×	19	297

Countries citing papers authored by Yulong Lu

Since Specialization

Citations

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

Fields of papers citing papers by Yulong Lu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yulong Lu

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

All Works

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20 of 20 papers shown

Lu, Jianfeng, et al.. (2024). Fully discretized Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem. Mathematics of Computation. 5 indexed citations

Yang, Yahong & Yulong Lu. (2024). Near-optimal deep neural network approximation for Korobov functions with respect to Lp and H1 norms. Neural Networks. 180. 106702–106702. 2 indexed citations

Lu, Jianfeng, et al.. (2024). On the Convergence of Sobolev Gradient Flow for the Gross–Pitaevskii Eigenvalue Problem. SIAM Journal on Numerical Analysis. 62(2). 667–691. 9 indexed citations

Lu, Yulong, Dejan Slepčev, & Lihan Wang. (2023). Birth–death dynamics for sampling: global convergence, approximations and their asymptotics. Nonlinearity. 36(11). 5731–5772. 5 indexed citations

Lu, Yulong, et al.. (2023). Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 10629–10636. 12 indexed citations

Wang, Danni, Xiaohong Li, Yulong Lu, et al.. (2023). An updated patent review of glutaminase inhibitors (2019–2022). Expert Opinion on Therapeutic Patents. 33(1). 17–28. 13 indexed citations

Lu, Jianfeng & Yulong Lu. (2022). A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems. 2(1). 1–21. 13 indexed citations

Lu, Yulong, et al.. (2022). Solving multiscale steady radiative transfer equation using neural networks with uniform stability. Research in the Mathematical Sciences. 9(3). 10 indexed citations

Lu, Yulong, Jianfeng Lu, & Min Wang. (2021). A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Partial Differential Equations. Conference on Learning Theory. 3196–3241. 10 indexed citations

10.

Lu, Yulong & Jianfeng Lu. (2020). A Universal Approximation Theorem of Deep Neural Networks for Expressing Distributions.. arXiv (Cornell University). 6 indexed citations

11.

Lu, Yiping, Chao Ma, Yulong Lu, Jianfeng Lu, & Lexing Ying. (2020). A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth. International Conference on Machine Learning. 1. 6426–6436. 4 indexed citations

12.

Lu, Jianfeng, Yulong Lu, & Zhennan Zhou. (2020). Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics. Journal of Computational Physics. 423. 109788–109788. 1 indexed citations

13.

Lu, Yulong & Jonathan C. Mattingly. (2019). Geometric ergodicity of Langevin dynamics with Coulomb interactions. Nonlinearity. 33(2). 675–699. 14 indexed citations

14.

Lu, Jianfeng, Yulong Lu, & James Nolen. (2019). Scaling Limit of the Stein Variational Gradient Descent: The Mean Field Regime. SIAM Journal on Mathematical Analysis. 51(2). 648–671. 33 indexed citations

15.

Wang, Dengfeng, et al.. (2018). Resolution of HEV Battery Cooling System Inlet Noise Issue by Optimizing Duct Design and Fan Speed Control Strategy. SAE International Journal of Engines. 11(1). 67–78. 2 indexed citations

16.

Liu, Yang, Hao Yu, & Yulong Lu. (2018). Improved Design of Risk Assessment Model for PPP Project under the Development of Marine Architecture. Journal of Coastal Research. 83(sp1). 74–74. 5 indexed citations

17.

Lu, Yulong, Andrew M. Stuart, & Hendrik Weber. (2017). Gaussian Approximations for Transition Paths in Brownian Dynamics. SIAM Journal on Mathematical Analysis. 49(4). 3005–3047. 8 indexed citations

18.

Lu, Yulong, Andrew M. Stuart, & Hendrik Weber. (2017). Gaussian Approximations for Probability Measures on $R^d$. SIAM/ASA Journal on Uncertainty Quantification. 5(1). 1136–1165. 10 indexed citations

19.

Lu, Yulong, Andrew M. Stuart, & Hendrik Weber. (2016). Gaussian approximations for transition paths in molecular dynamics. CaltechAUTHORS (California Institute of Technology). 2 indexed citations

20.

Hu, Guanghui, Yulong Lu, & Bo Zhang. (2013). The factorization method for inverse elastic scattering from periodic structures. Inverse Problems. 29(11). 115005–115005. 18 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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