Guang Lin
Impact in
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- Probabilistic and Robust Engineering Design
- Statistical and Nonlinear Physics top 0.5%
- Model Reduction and Neural Networks
Papers in
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- Probabilistic and Robust Engineering Design 55
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- Model Reduction and Neural Networks 46
- Co-authors
- George Em KarniadakisZiyang HuangArezoo M. ArdekaniSheng ZhangChristian MoyaWeixuan LiZecheng ZhangBledar A. Konomi
- Journals
- Journal of Computational Physics (41 papers)Journal of Computational and Applied Mathematics (8 papers)International Journal for Uncertainty Quantification (4 papers)Multiscale Modeling and Simulation (4 papers)Journal of Scientific Computing (4 papers)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Guang Lin
204 papers receiving 4.0k citations
Peers
Comparison fields: 5 of 154
- Statistics, Probability and Uncertainty 693
- Statistical and Nonlinear Physics 798
- Environmental Engineering 659
- Modeling and Simulation 176
- Computational Mechanics 764
Countries citing papers authored by Guang Lin
This map shows the geographic impact of Guang Lin'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 Guang Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guang Lin more than expected).
Fields of papers citing papers by Guang Lin
This network shows the impact of papers produced by Guang Lin. 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 Guang Lin. The network helps show where Guang Lin may publish in the future.
Co-authors
The 25 scholars most cited alongside Guang Lin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 10 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 4 | |
| 4 | 2024 | 11 | |
| 5 | 2024 | 13 | |
| 6 | 2024 | 4 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 17 | |
| 9 | 2023 | 5 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 28 | |
| 12 | 2022 | 10 | |
| 13 | 2022 | 9 | |
| 14 | 2022 | 14 | |
| 15 | 2021 | 29 | |
| 16 | 2021 | 31 | |
| 17 | 2020 | 8 | |
| 18 | A Sparse Deep Factorization Machine for Efficient CTR prediction | 2020 | 2 |
| 19 | Robust data-driven discovery of governing physical laws using a new subsampling-based sparse Bayesian method to tackle four challenges (large noise, outliers, data integration, and extrapolation) | 2019 | 5 |
| 20 | 2018 | 19 |
About Guang Lin
Guang Lin is a scholar working on Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics, Computational Mechanics, Environmental Engineering and Computational Theory and Mathematics, having authored 224 papers that have together received 4.2k indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (55 papers), Model Reduction and Neural Networks (46 papers), Advanced Numerical Methods in Computational Mathematics (18 papers), Power System Optimization and Stability (17 papers), Gaussian Processes and Bayesian Inference (16 papers), Computational Fluid Dynamics and Aerodynamics (14 papers), Advanced Multi-Objective Optimization Algorithms (13 papers) and Fluid Dynamics and Turbulent Flows (13 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (693 citations), Statistical and Nonlinear Physics (798 citations), Environmental Engineering (659 citations), Modeling and Simulation (176 citations) and Computational Mechanics (764 citations). Guang Lin has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include George Em Karniadakis, Ziyang Huang, Arezoo M. Ardekani, Sheng Zhang, Christian Moya, Weixuan Li, Zecheng Zhang, Bledar A. Konomi, J. Nathan Kutz and Ido Bright. Their work appears in journals such as Journal of Computational Physics, Journal of Computational and Applied Mathematics, International Journal for Uncertainty Quantification, Multiscale Modeling and Simulation and Journal of Scientific Computing.
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.