Lu Lu

12.1k total citations · 7 hit papers
93 papers, 7.4k citations indexed

About

Lu Lu is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Lu Lu has authored 93 papers receiving a total of 7.4k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Statistical and Nonlinear Physics, 19 papers in Artificial Intelligence and 16 papers in Electrical and Electronic Engineering. Recurrent topics in Lu Lu's work include Model Reduction and Neural Networks (27 papers), Erythrocyte Function and Pathophysiology (9 papers) and Blood properties and coagulation (9 papers). Lu Lu is often cited by papers focused on Model Reduction and Neural Networks (27 papers), Erythrocyte Function and Pathophysiology (9 papers) and Blood properties and coagulation (9 papers). Lu Lu collaborates with scholars based in United States, China and United Kingdom. Lu Lu's co-authors include George Em Karniadakis, Paris Perdikaris, Sifan Wang, Liu Yang, Ioannis G. Kevrekidis, Min Zhu, Xuhui Meng, Dongkun Zhang, Ling Guo and Chenxi Wu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Lu Lu

80 papers receiving 7.2k citations

Hit Papers

5 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.

Physics-informed machine learning

2021 3.8k citations George Em Karniadakis, Ioannis G. Kevrekidis et al. Nature Reviews Physics profile →
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

2022 388 citations Lu Lu, George Em Karniadakis et al. Computer Methods in Applied Mechanics and Engineering profile →
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

2022 319 citations Chenxi Wu, Min Zhu et al. Computer Methods in Applied Mechanics and Engineering profile →
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

2019 318 citations Lu Lu, George Em Karniadakis et al. Journal of Computational Physics profile →
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

2021 287 citations Lu Lu profile →
Extraction of mechanical properties of materials through deep learning from instrumented indentation

2020 242 citations Lu Lu, Ming Dao et al. Proceedings of the National Academy of Sciences profile →
MIONet: Learning Multiple-Input Operators via Tensor Product

2022 109 citations Lu Lu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lu Lu United States	30	3.2k	1.3k	1.2k	894	886	93	7.4k
Sifan Wang United States	11	3.9k 1.2×	1.7k 1.3×	1.3k 1.0×	1.0k 1.1×	744 0.8×	21	7.1k
Jianxun Wang China	35	1.6k 0.5×	1.2k 0.9×	446 0.4×	753 0.8×	492 0.6×	184	5.2k
Linda Petzold United States	55	1.8k 0.6×	1.8k 1.4×	403 0.3×	819 0.9×	1.0k 1.1×	253	12.9k
Dongbin Xiu United States	38	2.6k 0.8×	2.1k 1.7×	618 0.5×	411 0.5×	783 0.9×	116	11.6k
Maziar Raissi United States	15	7.8k 2.5×	3.3k 2.6×	2.5k 2.0×	1.9k 2.2×	1.3k 1.4×	32	14.4k
Andrew M. Stuart United Kingdom	49	1.8k 0.6×	1.7k 1.3×	2.0k 1.6×	271 0.3×	284 0.3×	208	9.7k
Nicholas Zabaras United States	45	1.5k 0.5×	1.4k 1.1×	683 0.6×	1.7k 1.9×	284 0.3×	160	7.4k
Allen Tannenbaum United States	55	869 0.3×	1.9k 1.5×	1.0k 0.8×	604 0.7×	551 0.6×	428	15.6k
Gianluigi Rozza Italy	39	5.3k 1.7×	3.8k 3.0×	424 0.3×	612 0.7×	561 0.6×	220	7.8k

Countries citing papers authored by Lu Lu

Since Specialization

Citations

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

Fields of papers citing papers by Lu Lu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lu Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Lu Lu. A scholar is included among the top collaborators of Lu 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 Lu Lu. Lu Lu 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

Jiao, Anran, et al.. (2025). One-shot learning for solution operators of partial differential equations. Nature Communications. 16(1). 8386–8386.

Zhang, Zecheng, Christian Moya, Lu Lu, Guang Lin, & Hayden Schaeffer. (2025). DeepONet as a multi-Operator extrapolation model: Distributed pretraining with physics-Informed fine-Tuning. Journal of Computational Physics. 547. 114537–114537.

Xia, Xiaobo, Kuai Fang, Lu Lu, et al.. (2025). Identifying trustworthiness challenges in deep learning models for continental-scale water quality prediction. SHILAP Revista de lepidopterología. 2(4). 100104–100104.

Zhang, Zecheng, Christian Moya, Lu Lu, Guang Lin, & Hayden Schaeffer. (2024). D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators. Computer Methods in Applied Mechanics and Engineering. 428. 117084–117084. 11 indexed citations

Wang, Eric, et al.. (2024). Speeding up and reducing memory usage for scientific machine learning via mixed precision. Computer Methods in Applied Mechanics and Engineering. 428. 117093–117093. 6 indexed citations

Cai, Shengze, et al.. (2024). Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta. PubMed. 1(2). 100016–100016. 8 indexed citations

Zhang, Yu, Yuhao Qiang, He Li, et al.. (2024). Signaling-biophysical modeling unravels mechanistic control of red blood cell phagocytosis by macrophages in sickle cell disease. PNAS Nexus. 3(2). pgae031–pgae031. 8 indexed citations

Yin, Minglang, et al.. (2024). A scalable framework for learning the geometry-dependent solution operators of partial differential equations. Nature Computational Science. 4(12). 928–940. 7 indexed citations

Jiao, Anran, et al.. (2024). Deep Learning for Solving and Estimating Dynamic Macro-finance Models. Computational Economics. 65(6). 3885–3921. 3 indexed citations

10.

Liu, Xin‐Yang, Min Zhu, Lu Lu, Hao Sun, & Jianxun Wang. (2024). Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics. Communications Physics. 7(1). 30 indexed citations

11.

Wu, Wenzhao, et al.. (2023). Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics. Applied Mathematics and Mechanics. 44(7). 1039–1068. 31 indexed citations

12.

Qu, Xinghua, et al.. (2023). AudioQR: Deep Neural Audio Watermarks For QR Code. 6192–6200. 2 indexed citations

13.

Lu, Lu, Wenlin Yuan, Hongshi Xu, et al.. (2023). Evaluation of the Complementary Characteristics for Wind-Photovoltaic-Hydro Hybrid System Considering Multiple Uncertainties in the Medium and Long Term. Water Resources Management. 38(2). 793–814. 5 indexed citations

14.

Wu, Chenxi, et al.. (2022). A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering. 403. 115671–115671. 319 indexed citations breakdown →

15.

Li, He, Zixiang Liu, Lu Lu, Pierre Buffet, & George Em Karniadakis. (2021). How the spleen reshapes and retains young and old red blood cells: A computational investigation. PLoS Computational Biology. 17(11). e1009516–e1009516. 31 indexed citations

16.

Karniadakis, George Em, Ioannis G. Kevrekidis, Lu Lu, et al.. (2021). Physics-informed machine learning. Nature Reviews Physics. 3(6). 422–440. 3772 indexed citations breakdown →

17.

Lu, Lu, Ming Dao, Punit Kumar, et al.. (2020). Extraction of mechanical properties of materials through deep learning from instrumented indentation. Proceedings of the National Academy of Sciences. 117(13). 7052–7062. 242 indexed citations breakdown →

18.

Lu, Lu, Xuejin Li, Pierre Buffet, et al.. (2018). Mechanics of diseased red blood cells in human spleen and consequences for hereditary blood disorders. Proceedings of the National Academy of Sciences. 115(38). 9574–9579. 101 indexed citations

19.

Lu, Lu, He Li, Xin Bian, Xuejin Li, & George Em Karniadakis. (2017). Mesoscopic Adaptive Resolution Scheme toward Understanding of Interactions between Sickle Cell Fibers. Biophysical Journal. 113(1). 48–59. 13 indexed citations

20.

Lu, Lu, Xuejin Li, Peter G. Vekilov, & George Em Karniadakis. (2016). Probing the Twisted Structure of Sickle Hemoglobin Fibers via Particle Simulations. Biophysical Journal. 110(9). 2085–2093. 21 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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