Jiang-Lun Wu

1.8k total citations
94 papers, 1.1k citations indexed

About

Jiang-Lun Wu is a scholar working on Finance, Applied Mathematics and Mathematical Physics. According to data from OpenAlex, Jiang-Lun Wu has authored 94 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Finance, 36 papers in Applied Mathematics and 29 papers in Mathematical Physics. Recurrent topics in Jiang-Lun Wu's work include Stochastic processes and financial applications (63 papers), Advanced Mathematical Modeling in Engineering (17 papers) and Stability and Controllability of Differential Equations (16 papers). Jiang-Lun Wu is often cited by papers focused on Stochastic processes and financial applications (63 papers), Advanced Mathematical Modeling in Engineering (17 papers) and Stability and Controllability of Differential Equations (16 papers). Jiang-Lun Wu collaborates with scholars based in United Kingdom, China and Germany. Jiang-Lun Wu's co-authors include Sergio Albeverio, Yong Xu, Bin Pei, Tusheng Zhang, Zdzisław Brzeźniak, Hanno Gottschalk, Aubrey Truman, Guangying Lv, Weisi Guo and Guangjun Shen and has published in prestigious journals such as Journal of Medicinal Chemistry, Physics Letters B and Communications in Mathematical Physics.

In The Last Decade

Jiang-Lun Wu

80 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jiang-Lun Wu United Kingdom 17 610 305 279 230 218 94 1.1k
Jaime San Martı́n Chile 18 692 1.1× 216 0.7× 455 1.6× 109 0.5× 129 0.6× 84 1.4k
Tadahisa Funaki Japan 17 384 0.6× 234 0.8× 542 1.9× 165 0.7× 113 0.5× 54 1.0k
Zoran Vondraček Croatia 19 511 0.8× 745 2.4× 624 2.2× 177 0.8× 33 0.2× 58 1.5k
Huaizhong Zhao United Kingdom 16 403 0.7× 130 0.4× 161 0.6× 112 0.5× 234 1.1× 49 641
Erkan Nane United States 18 319 0.5× 494 1.6× 280 1.0× 741 3.2× 114 0.5× 55 1.1k
Pao–Liu Chow United States 14 431 0.7× 169 0.6× 176 0.6× 85 0.4× 296 1.4× 30 804
Mihály Kovács Sweden 20 422 0.7× 170 0.6× 165 0.6× 354 1.5× 93 0.4× 66 1.1k
Evelyn Buckwar Austria 19 699 1.1× 197 0.6× 85 0.3× 344 1.5× 141 0.6× 47 1.4k
John A. D. Appleby Ireland 13 235 0.4× 244 0.8× 105 0.4× 108 0.5× 316 1.4× 69 703
Henri Schurz United States 16 579 0.9× 79 0.3× 94 0.3× 135 0.6× 80 0.4× 52 1.1k

Countries citing papers authored by Jiang-Lun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Jiang-Lun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jiang-Lun Wu. 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 Jiang-Lun Wu. The network helps show where Jiang-Lun Wu may publish in the future.

Co-authorship network of co-authors of Jiang-Lun Wu

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

All Works

20 of 20 papers shown
1.
Wu, Jiang-Lun, et al.. (2024). Averaging principle for reflected stochastic evolution equations. Applied Mathematics Letters. 160. 109311–109311.
2.
Shen, Guangjun, Huan‐Xiang Zhou, & Jiang-Lun Wu. (2024). Large deviation principle for multi-scale distribution-dependent stochastic differential equations driven by fractional Brownian motions. Journal of Evolution Equations. 24(2). 1 indexed citations
3.
4.
Lv, Guangying, et al.. (2022). On the Campanato and Hölder regularity of local and nonlocal stochastic diffusion equations. Discrete and Continuous Dynamical Systems - B. 28(2). 1244–1244. 2 indexed citations
5.
Qiao, Huijie & Jiang-Lun Wu. (2022). Path independence of the additive functionals for stochastic differential equations driven by G-lévy processes. 7(2). 101–118. 1 indexed citations
6.
Shen, Guangjun, et al.. (2021). Stochastic averaging principle for distribution dependent stochastic differential equations. Applied Mathematics Letters. 125. 107761–107761. 6 indexed citations
7.
Huang, Qiao, Jinqiao Duan, & Jiang-Lun Wu. (2018). Maximum principles for nonlocal parabolic Waldenfels operators. Bulletin of Mathematical Sciences. 2 indexed citations
8.
Lv, Guangying & Jiang-Lun Wu. (2018). Heterogeneous stochastic scalar conservation laws with non-homogeneous Dirichlet boundary conditions. Journal of Hyperbolic Differential Equations. 15(2). 291–328. 2 indexed citations
9.
Zou, Guang‐an, Guangying Lv, & Jiang-Lun Wu. (2018). On the regularity of weak solutions to space–time fractional stochastic heat equations. Statistics & Probability Letters. 139. 84–89. 7 indexed citations
10.
Pei, Bin, Yong Xu, & Jiang-Lun Wu. (2016). Two-time-scales hyperbolic–parabolic equations driven by Poisson random measures: Existence, uniqueness and averaging principles. Journal of Mathematical Analysis and Applications. 447(1). 243–268. 36 indexed citations
11.
Lv, Guangying & Jiang-Lun Wu. (2016). Renormalized entropy solutions of stochastic scalar conservation laws with boundary condition. Journal of Functional Analysis. 271(8). 2308–2338. 3 indexed citations
12.
Zhang, Hai, et al.. (2015). Model selection and estimation in high dimensional regression models with group SCAD. Statistics & Probability Letters. 103. 86–92. 13 indexed citations
13.
Wu, Jiang-Lun, et al.. (2014). New sufficient conditions of existence, moment estimations and non confluence for SDEs with non-Lipschitzian coefficients. Stochastic Processes and their Applications. 124(12). 4030–4049. 19 indexed citations
14.
Wu, Jiang-Lun, et al.. (2011). On a Burgers type nonlinear equation perturbed by a pure jump Lévy noise in Rd. Bulletin des Sciences Mathématiques. 136(5). 484–506. 16 indexed citations
15.
Wu, Jiang-Lun & Wei Yang. (2010). Pricing CDO tranches in an intensity based model with the mean reversion approach. Mathematical and Computer Modelling. 52(5-6). 814–825. 7 indexed citations
16.
Albeverio, Sergio, Zdzisław Brzeźniak, & Jiang-Lun Wu. (2010). Existence of global solutions and invariant measures for stochastic differential equations driven by Poisson type noise with non-Lipschitz coefficients. Journal of Mathematical Analysis and Applications. 371(1). 309–322. 83 indexed citations
17.
Wang, Miao, Dexin Guan, Siqin Han, & Jiang-Lun Wu. (2009). Comparison of eddy covariance and chamber-based methods for measuring CO2 flux in a temperate mixed forest. Tree Physiology. 30(1). 149–163. 55 indexed citations
18.
Truman, Aubrey & Jiang-Lun Wu. (2006). On a stochastic nonlinear equation arising from 1D integro-differential scalar conservation laws. Journal of Functional Analysis. 238(2). 612–635. 13 indexed citations
19.
Truman, Aubrey & Jiang-Lun Wu. (2003). STOCHASTIC BURGERS EQUATION WITH LÉVY SPACE-TIME WHITE NOISE. 298–323. 28 indexed citations
20.
Applebaum, David & Jiang-Lun Wu. (2000). Stochastic partial differential equations driven by Lévy space-time white noise. Random Operators and Stochastic Equations. 8(3). 245–260. 30 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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