G. Wang

905 total citations
2 papers, 5 citations indexed

About

G. Wang is a scholar working on Control and Systems Engineering, Artificial Intelligence and Nuclear and High Energy Physics. According to data from OpenAlex, G. Wang has authored 2 papers receiving a total of 5 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Control and Systems Engineering, 1 paper in Artificial Intelligence and 1 paper in Nuclear and High Energy Physics. Recurrent topics in G. Wang's work include Statistical Methods and Bayesian Inference (1 paper), Machine Learning and ELM (1 paper) and High-Energy Particle Collisions Research (1 paper). G. Wang is often cited by papers focused on Statistical Methods and Bayesian Inference (1 paper), Machine Learning and ELM (1 paper) and High-Energy Particle Collisions Research (1 paper). G. Wang collaborates with scholars based in United States, Pakistan and China. G. Wang's co-authors include Z. Tang, Xilin Shi, Shuang Ma, Yinping Li, Wenjie Xu, Liangtong Zhan, Duanyang Zhuang, Yunmin Chen and Jinlong Li and has published in prestigious journals such as Energy and Physical Review C.

In The Last Decade

G. Wang

2 papers receiving 5 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
G. Wang United States	2	3	2	1	1	1	2	5
A. Borysenko Ukraine	2	3 1.0×	2 1.0×		1 1.0×	1 1.0×	2	5
Alessandro Thea Switzerland	2	3 1.0×	2 1.0×				4	3
J. Silva Brazil	2	3 1.0×	2 1.0×				2	4
K. Klimek Switzerland	2	3 1.0×	2 1.0×				2	4
E. J. Staats Japan	2	3 1.0×	2 1.0×				5	4
T. Miao China	3	4 1.3×	2 1.0×	1 1.0×			8	9
M. Pinamonti Switzerland	1	3 1.0×	2 1.0×				2	3
S. Hall United Kingdom	1	4 1.3×	2 1.0×				2	5
S. Valentinetti Italy	2	4 1.3×	2 1.0×				2	5
C. Rizzi Switzerland	2	2 0.7×	2 1.0×				3	4

Countries citing papers authored by G. Wang

Since Specialization

Citations

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

Fields of papers citing papers by G. Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G. Wang

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

All Works

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2 of 2 papers shown

1.

Wang, G., Jinlong Li, Shuang Ma, et al.. (2024). Geometry prediction and design for energy storage salt caverns using artificial neural network. Energy. 308. 132820–132820. 3 indexed citations

2.

Tang, Z. & G. Wang. (2013). Effects of the detection efficiency on multiplicity distributions. Physical Review C. 88(2). 2 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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