Chao Yang

157 papers receiving 6.0k citations

Hit Papers

GW100: Benchmarking G0W0 for Molecular Systems 2015 · 292 citations
292199820262007201650010001.5k2.0k

Peers

Chao Yang
Comparison fields: 5 of 156
  • Structural Biology 250
  • Computational Mathematics 87
  • Numerical Analysis 392
  • Atomic and Molecular Physics, and Optics 2.1k
  • Computational Theory and Mathematics 863
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Matteo Frigo United States
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Citations per year

Countries citing papers authored by Chao Yang

Since Specialization
Citations

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

Fields of papers citing papers by Chao Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Chao Yang, 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 Chao Yang Line = papers co-authored together Chao Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20252
2 20240
3 20243
4 20242
5 20246
6 20234
7 20232
8 20236
9 20217
10
Evaluations of the Gap between Supervised and Reinforcement Lifelong Learning on Robotic Manipulation Tasks
20211
11 202029
12 20207
13 201918
14 201818
15 201781
16 201613
17 20142
18 201164
19
OPTIMIZING THE RULE CURVES FOR MULTI-RESERVOIR OPERATIONS USING A GENETIC ALGORITHM AND HEC-5
20029
20
ENHANCED EFFICIENCY ON THE PARAMETERS ESTIMATION OF MUSKINGUM MODEL USING ARTIFICIAL NEURAL NETWORK
20013

About Chao Yang

Chao Yang is a scholar working on Computational Mathematics, Structural Biology, Hardware and Architecture, Computational Theory and Mathematics and Numerical Analysis, having authored 168 papers that have together received 6.3k indexed citations. Recurring topics across this work include Matrix Theory and Algorithms (35 papers), Advanced Chemical Physics Studies (31 papers), Advanced NMR Techniques and Applications (19 papers), Parallel Computing and Optimization Techniques (18 papers), Machine Learning in Materials Science (16 papers), Stochastic processes and financial applications (13 papers), Distributed and Parallel Computing Systems (12 papers) and Spectroscopy and Quantum Chemical Studies (11 papers). The work is most often cited by research in Structural Biology (250 citations), Computational Mathematics (87 citations), Numerical Analysis (392 citations), Atomic and Molecular Physics, and Optics (2.1k citations) and Computational Theory and Mathematics (863 citations). Chao Yang has collaborated with scholars based in United States, China and Australia. Frequent co-authors include D. C. Sorensen, Richard B. Lehoucq, Lin Lin, Wei Hu, Jinlong Yang, Juan Meza, Esmond Ng, Pawel A. Penczek, Pieter Maris and James P. Vary. Their work appears in journals such as Journal of Chemical Theory and Computation, Computer Physics Communications, SIAM Journal on Scientific Computing, Journal of Computational Physics and ACM Transactions on Mathematical Software.

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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