Hanwen Wang
Impact in
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- Model Reduction and Neural Networks
- Computational Mechanics top 5%
- Fluid Dynamics and Turbulent Flows
Papers in
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- Model Reduction and Neural Networks 3
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- Fluid Dynamics and Turbulent Flows 2
- Co-authors
- Paris Perdikaris (4 shared papers)Sifan Wang (3 shared papers)Ellen Kuhl (1 shared paper)Kevin Linka (1 shared paper)Francisco Sahli Costabal (1 shared paper)Mathias Peirlinck (1 shared paper)Yingwei Li (2 shared papers)Wenjun Cui (2 shared papers)
- Journals
- Computer Methods in Applied Mechanics and Engineering (2 papers)Journal of Information Security and Applications (1 paper)ACS Nano (1 paper)IEEE Transactions on Biomedical Engineering (1 paper)Brain stimulation (1 paper)
- Partner nations
- ChinaUnited StatesMalaysia
In The Last Decade
Hanwen Wang
10 papers receiving 989 citations
Hanwen Wang's Hit Papers
Peers
Comparison fields: 5 of 88
- Statistical and Nonlinear Physics 649
- Computational Mechanics 266
- Statistics, Probability and Uncertainty 85
- Modeling and Simulation 36
- Artificial Intelligence 198
Countries citing papers authored by Hanwen Wang
This map shows the geographic impact of Hanwen 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 Hanwen Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hanwen Wang more than expected).
Fields of papers citing papers by Hanwen Wang
This network shows the impact of papers produced by Hanwen 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 Hanwen Wang. The network helps show where Hanwen Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Hanwen Wang, 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 | Learning the solution operator of parametric partial differential equations with physics-informed DeepONets Hit paper breakdown → | 2021 | 482 |
| 2 | On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Hit paper breakdown → | 2021 | 404 |
| 3 | 2022 | 71 | |
| 4 | 2021 | 38 | |
| 5 | 2025 | 6 | |
| 6 | 2023 | 3 | |
| 7 | 2024 | 3 | |
| 8 | 2025 | 2 | |
| 9 | 2025 | 1 | |
| 10 | 2020 | 1 | |
| 11 | 2024 | 0 | |
| 12 | 2024 | 0 |
About Hanwen Wang
Hanwen Wang is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics, Condensed Matter Physics, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 12 papers that have together received 1.0k indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (3 papers), Fluid Dynamics and Turbulent Flows (2 papers), Ultrasound and Hyperthermia Applications (2 papers), Heat Transfer and Optimization (1 paper), Numerical methods in engineering (1 paper), Advanced Mathematical Modeling in Engineering (1 paper), Ferroelectric and Piezoelectric Materials (1 paper) and Data-Driven Disease Surveillance (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (649 citations), Computational Mechanics (266 citations), Statistics, Probability and Uncertainty (85 citations), Modeling and Simulation (36 citations) and Artificial Intelligence (198 citations). Hanwen Wang has collaborated with scholars based in China, United States and Malaysia. Frequent co-authors include Paris Perdikaris, Sifan Wang, Ellen Kuhl, Kevin Linka, Francisco Sahli Costabal, Mathias Peirlinck, Yingwei Li, Wenjun Cui, Xiaoyu Zhang and Shouyang Yu. Their work appears in journals such as Computer Methods in Applied Mechanics and Engineering, Journal of Information Security and Applications, ACS Nano, IEEE Transactions on Biomedical Engineering and Brain stimulation.
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.