Han Gao
- Statistical and Nonlinear Physics top 0.5%
- Computational Mechanics top 2%
- Aerospace Engineering top 5%
- Mechanical Engineering top 10%
- Artificial Intelligence top 10%
- Topics
- Model Reduction and Neural Networks (5 papers)Probabilistic and Robust Engineering Design (3 papers)Fluid Dynamics and Vibration Analysis (2 papers)
- Cited by
- Statistical and Nonlinear PhysicsComputational MechanicsStatistics, Probability and Uncertainty
- Journals
- Journal of Computational PhysicsComputer Methods in Applied Mechanics and EngineeringPhysica D Nonlinear Phenomena
- Partner nations
- United StatesChina
In The Last Decade
Han Gao
6 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Statistical and Nonlinear Physics 884
- Computational Mechanics 479
- Aerospace Engineering 221
- Mechanical Engineering 206
- Artificial Intelligence 181
Countries citing papers authored by Han Gao
This map shows the geographic impact of Han Gao'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 Han Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Han Gao more than expected).
Fields of papers citing papers by Han Gao
This network shows the impact of papers produced by Han Gao. 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 Han Gao. The network helps show where Han Gao may publish in the future.
Co-authorship network of co-authors of Han Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Han Gao. A scholar is included among the top collaborators of Han Gao 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 Han Gao. Han Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problemsbreakdown → | 176 |
| 2 | 42 | |
| 3 | 27 | |
| 4 | 20 | |
| 5 | PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domainbreakdown → | 381 |
| 6 | Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation databreakdown → | 634 |
About Han Gao
Han Gao is a scholar working on Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics and Computational Mechanics, having authored 6 papers that have together received 1.3k indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (5 papers), Probabilistic and Robust Engineering Design (3 papers) and Fluid Dynamics and Vibration Analysis (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (884 citations), Computational Mechanics (479 citations) and Statistics, Probability and Uncertainty (143 citations). Han Gao has collaborated with scholars based in United States and China. Frequent co-authors include Jianxun Wang, Luning Sun, Shaowu Pan, Matthew J. Zahr, Xueyu Zhu, Jun Han, Chaoli Wang, Li Guo, Danny Z. Chen and Hao Zheng. Their work appears in journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and Physica D Nonlinear Phenomena.
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.