Khemraj Shukla
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- Model Reduction and Neural Networks 10
- Geophysics top 5%
- Seismic Waves and Analysis 13
- Seismic Imaging and Inversion Techniques 12
- Computational Mechanics top 5%
- Fluid Dynamics and Turbulent Flows 4
- Lattice Boltzmann Simulation Studies 3
- Computational Fluid Dynamics and Aerodynamics 3
- Artificial Intelligence top 10%
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- Drilling and Well Engineering 3
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- Image and Signal Denoising Methods 2
- Co-authors
- George Em KarniadakisChristian HuberVivek OommenRémi DingrevilleSomdatta GoswamiKenji KawaguchiZheyuan HuAmeya D. Jagtap
- Journals
- SHILAP Revista de lepidopterología (1 paper)Journal of Fluid Mechanics (1 paper)Journal of Computational Physics (3 papers)
- Partner nations
- United StatesItalySpain
In The Last Decade
Khemraj Shukla
31 papers receiving 860 citations
Hit Papers
Peers
Comparison fields: 5 of 89
- Statistical and Nonlinear Physics 352
- Geophysics 257
- Computational Mechanics 153
- Statistics, Probability and Uncertainty 45
- Artificial Intelligence 184
Countries citing papers authored by Khemraj Shukla
This map shows the geographic impact of Khemraj Shukla'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 Khemraj Shukla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Khemraj Shukla more than expected).
Fields of papers citing papers by Khemraj Shukla
This network shows the impact of papers produced by Khemraj Shukla. 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 Khemraj Shukla. The network helps show where Khemraj Shukla may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Khemraj Shukla, 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 | 2 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 21 | |
| 6 | 2024 | 25 | |
| 7 | Tackling the curse of dimensionality with physics-informed neural networksbreakdown → | 2024 | 72 |
| 8 | 2023 | 9 | |
| 9 | 2023 | 16 | |
| 10 | 2023 | 2 | |
| 11 | 2023 | 17 | |
| 12 | 2022 | 101 | |
| 13 | Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversionsbreakdown → | 2022 | 234 |
| 14 | 2021 | 15 | |
| 15 | 2020 | 10 | |
| 16 | 2020 | 3 | |
| 17 | 2018 | 3 | |
| 18 | 2017 | 1 | |
| 19 | 2014 | 2 | |
| 20 | 2008 | 21 |
About Khemraj Shukla
Khemraj Shukla is a scholar working on Geophysics, Statistical and Nonlinear Physics and Structural Biology, having authored 33 papers that have together received 892 indexed citations. Recurring topics across this work include Seismic Waves and Analysis (13 papers), Seismic Imaging and Inversion Techniques (12 papers), Model Reduction and Neural Networks (10 papers), Fluid Dynamics and Turbulent Flows (4 papers), Lattice Boltzmann Simulation Studies (3 papers), Drilling and Well Engineering (3 papers), Computational Fluid Dynamics and Aerodynamics (3 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (352 citations), Geophysics (257 citations) and Computational Mechanics (153 citations). Khemraj Shukla has collaborated with scholars based in United States, Italy and Spain. Frequent co-authors include George Em Karniadakis, Christian Huber, Vivek Oommen, Rémi Dingreville, Somdatta Goswami, Kenji Kawaguchi, Zheyuan Hu, Ameya D. Jagtap, James L. Blackshire and Sankar Kumar Nath. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Fluid Mechanics and Journal of Computational Physics.
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.