Bing Yu

1.4k citations
9 papers · 897 · 1 hit paper · h-index 4

Impact in

Papers in

Bing Yu

8 papers receiving 865 citations

Bing Yu's Hit Papers

The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems 2018 · 858 citations
8580+2+5Years since publication250500750

Peers

Bing Yu
Comparison fields: 5 of 77
  • Statistical and Nonlinear Physics 688
  • Statistics, Probability and Uncertainty 123
  • Computational Mechanics 299
  • Computational Mathematics 7
  • Numerical Analysis 61
Replace Zhu Wang with:
Zhu Wang United States
Hayden Schaeffer United States
Federico Negri Switzerland
Jens Berg Sweden
Deep Ray United States
Hanwen Wang China
Xueyu Zhu United States
Matthew J. Zahr United States
Kailiang Wu China
Matthew J. Colbrook United Kingdom
Bing Yu relative to Zhu Wang United States Zhu Wang's profile →
Citations per field
00.5×1.5×1.8×
Zhu Wang · 1×
Citations per year

Countries citing papers authored by Bing Yu

Since Specialization
Citations

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

Fields of papers citing papers by Bing Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Bing Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bing Yu Line = papers co-authored together Bing Yu links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems
Hit paper breakdown →
2018858
2 201215
3 201211
4 20187
5 20172
6 20182
7 20151
8 20181
9 20250

About Bing Yu

Bing Yu is a scholar working on Instrumentation, Electrical and Electronic Engineering, Ophthalmology, Biomedical Engineering and Environmental Engineering, having authored 9 papers that have together received 897 indexed citations. Recurring topics across this work include Advanced Optical Sensing Technologies (5 papers), Optical Systems and Laser Technology (3 papers), Ocular and Laser Science Research (3 papers), Optical Coherence Tomography Applications (2 papers), Soil Moisture and Remote Sensing (1 paper), Image Enhancement Techniques (1 paper), Advanced Fiber Optic Sensors (1 paper) and Synthetic Aperture Radar (SAR) Applications and Techniques (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (688 citations), Statistics, Probability and Uncertainty (123 citations), Computational Mechanics (299 citations), Computational Mathematics (7 citations) and Numerical Analysis (61 citations). Bing Yu has collaborated with scholars based in China, United States and Spain. Frequent co-authors include E Weinan, Hongru Yang, Lei Wu, Gaoping Li, Bin Yang, Chao Chen, Xiaopeng Wang, Rui Zhang, Keren Dai and Lihong Niu. Their work appears in journals such as Applied Optics, IEEE Sensors Journal, Communications in Mathematics and Statistics, National Remote Sensing Bulletin and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.

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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