Mohammad Sadegh Eshaghi
- Civil and Structural Engineering top 10%
- Topology Optimization in Engineering 3
- Structural Health Monitoring Techniques 3
- Dam Engineering and Safety 3
- Hydraulic flow and structures 3
- Seismic and Structural Analysis of Tall Buildings 2
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- Hydrological Forecasting Using AI 3
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- Model Reduction and Neural Networks 4
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- Neural Networks and Applications 3
Mohammad Sadegh Eshaghi
17 papers receiving 343 citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Civil and Structural Engineering 144
- Environmental Engineering 59
- Statistical and Nonlinear Physics 46
- Water Science and Technology 46
- Safety, Risk, Reliability and Quality 24
Countries citing papers authored by Mohammad Sadegh Eshaghi
This map shows the geographic impact of Mohammad Sadegh Eshaghi'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 Mohammad Sadegh Eshaghi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Sadegh Eshaghi more than expected).
Fields of papers citing papers by Mohammad Sadegh Eshaghi
This network shows the impact of papers produced by Mohammad Sadegh Eshaghi. 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 Mohammad Sadegh Eshaghi. The network helps show where Mohammad Sadegh Eshaghi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mohammad Sadegh Eshaghi, 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 | Variational Physics-informed Neural Operator (VINO) for solving partial differential equationsbreakdown → | 2025 | 34 |
| 2 | 2025 | 9 | |
| 3 | Kolmogorov–Arnold-Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold Networksbreakdown → | 2024 | 53 |
| 4 | 2024 | 14 | |
| 5 | 2024 | 35 | |
| 6 | 2024 | 6 | |
| 7 | 2022 | 9 | |
| 8 | 2022 | 2 | |
| 9 | 2022 | 10 | |
| 10 | 2021 | 8 | |
| 11 | 2021 | 2 | |
| 12 | 2021 | 20 | |
| 13 | 2021 | 7 | |
| 14 | 2020 | 29 | |
| 15 | 2019 | 46 | |
| 16 | 2019 | 42 | |
| 17 | 2018 | 35 |
About Mohammad Sadegh Eshaghi
Mohammad Sadegh Eshaghi is a scholar working on Civil and Structural Engineering, Statistical and Nonlinear Physics and Environmental Engineering, having authored 17 papers that have together received 361 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (4 papers), Hydrological Forecasting Using AI (3 papers), Topology Optimization in Engineering (3 papers), Structural Health Monitoring Techniques (3 papers), Neural Networks and Applications (3 papers), Dam Engineering and Safety (3 papers), Hydraulic flow and structures (3 papers) and Seismic and Structural Analysis of Tall Buildings (2 papers). The work is most often cited by research in Civil and Structural Engineering (144 citations), Environmental Engineering (59 citations) and Statistical and Nonlinear Physics (46 citations). Mohammad Sadegh Eshaghi has collaborated with scholars based in Iran, Germany and China. Frequent co-authors include Cosmin Anitescu, Isa Ebtehaj, Hossein Bonakdari, Mir Jafar Sadegh Safari, Timon Rabczuk, Yizheng Wang, Timon Rabczuk, Aydin Shishegaran, Mohammad Sadegh Barkhordari and Xiaoying Zhuang. Their work appears in journals such as Journal of Hydrology, Computer Methods in Applied Mechanics and Engineering and Neurocomputing.
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.