Khemraj Shukla

7.8k total citations · 3 hit papers
33 papers, 892 citations indexed

About

Khemraj Shukla is a scholar working on Geophysics, Statistical and Nonlinear Physics and Computational Mechanics. According to data from OpenAlex, Khemraj Shukla has authored 33 papers receiving a total of 892 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Geophysics, 10 papers in Statistical and Nonlinear Physics and 8 papers in Computational Mechanics. Recurrent topics in Khemraj Shukla's work include Seismic Waves and Analysis (13 papers), Seismic Imaging and Inversion Techniques (12 papers) and Model Reduction and Neural Networks (10 papers). Khemraj Shukla is often cited by papers focused on Seismic Waves and Analysis (13 papers), Seismic Imaging and Inversion Techniques (12 papers) and Model Reduction and Neural Networks (10 papers). Khemraj Shukla collaborates with scholars based in United States, Italy and Canada. Khemraj Shukla's co-authors include George Em Karniadakis, Christian Huber, Vivek Oommen, Rémi Dingreville, Somdatta Goswami, Kenji Kawaguchi, Zheyuan Hu, James L. Blackshire, Ameya D. Jagtap and Sankar Kumar Nath and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Fluid Mechanics and Journal of Computational Physics.

In The Last Decade

Khemraj Shukla

31 papers receiving 860 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions

2022 234 citations Christian Huber, Khemraj Shukla et al. Journal of Geophysical Research Solid Earth profile →
Tackling the curse of dimensionality with physics-informed neural networks

2024 72 citations Khemraj Shukla, George Em Karniadakis et al. profile →
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

2024 58 citations Khemraj Shukla, George Em Karniadakis et al. Computer Methods in Applied Mechanics and Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Khemraj Shukla United States	16	352	257	184	153	116	33	892
Kamyar Azizzadenesheli United States	14	321 0.9×	223 0.9×	326 1.8×	174 1.1×	103 0.9×	36	1.1k
David A. Barajas‐Solano United States	12	461 1.3×	109 0.4×	143 0.8×	177 1.2×	55 0.5×	31	887
My Ha Dao Singapore	13	181 0.5×	115 0.4×	64 0.3×	174 1.1×	42 0.4×	33	599
Hanwen Wang China	5	604 1.7×	53 0.2×	192 1.0×	262 1.7×	84 0.7×	11	908
Pin Wu China	18	159 0.5×	104 0.4×	90 0.5×	260 1.7×	43 0.4×	76	995
Nikola Kovachki United States	8	184 0.5×	44 0.2×	137 0.7×	97 0.6×	54 0.5×	12	518
Umair bin Waheed Saudi Arabia	19	234 0.7×	889 3.5×	371 2.0×	78 0.5×	137 1.2×	117	1.3k
Bing Yu China	4	655 1.9×	28 0.1×	160 0.9×	293 1.9×	165 1.4×	9	848
Carlos Ortiz Marrero United States	5	179 0.5×	52 0.2×	274 1.5×	70 0.5×	20 0.2×	11	596

Countries citing papers authored by Khemraj Shukla

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Khemraj Shukla

This figure shows the co-authorship network connecting the top 25 collaborators of Khemraj Shukla. A scholar is included among the top collaborators of Khemraj Shukla 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 Khemraj Shukla. Khemraj Shukla is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Shukla, Khemraj, et al.. (2025). Optimizing the optimizer for physics-informed neural networks and Kolmogorov-Arnold networks. Computer Methods in Applied Mechanics and Engineering. 446. 118308–118308. 2 indexed citations

Kooshkbaghi, Mahdi, Khemraj Shukla, Zhen Li, et al.. (2024). Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs. Journal of Fluid Mechanics. 985. 4 indexed citations

Oommen, Vivek, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, & George Em Karniadakis. (2024). Rethinking materials simulations: Blending direct numerical simulations with neural operators. npj Computational Materials. 10(1). 21 indexed citations

Molina-Ruiz, M., Khemraj Shukla, A. Ananyeva, et al.. (2024). Low mechanical loss and high refractive index in amorphous Ta2O5 films grown by magnetron sputtering. Physical Review Materials. 8(3). 1 indexed citations

Chan, Jesse, et al.. (2024). High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations. Journal of Computational Physics. 504. 112876–112876. 2 indexed citations

Florio, Mario De, et al.. (2024). AI-Aristotle: A physics-informed framework for systems biology gray-box identification. PLoS Computational Biology. 20(3). e1011916–e1011916. 25 indexed citations

Cobelli, Pablo, et al.. (2023). Physics informed neural networks for wind field modeling in wind farms. Journal of Physics Conference Series. 2505(1). 12051–12051. 9 indexed citations

Shukla, Khemraj, et al.. (2023). High-Order Methods for Hypersonic Flows with Strong Shocks and Real Chemistry. SSRN Electronic Journal. 2 indexed citations

Shukla, Khemraj, et al.. (2023). A framework based on symbolic regression coupled with eXtended Physics-Informed Neural Networks for gray-box learning of equations of motion from data. Computer Methods in Applied Mechanics and Engineering. 415. 116258–116258. 16 indexed citations

10.

Kumar, Varun, et al.. (2023). MYCRUNCHGPT: A LLM ASSISTED FRAMEWORK FOR SCIENTIFIC MACHINE LEARNING. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 4(4). 41–72. 17 indexed citations

11.

Shukla, Khemraj, et al.. (2023). High-order methods for hypersonic flows with strong shocks and real chemistry. Journal of Computational Physics. 490. 112310–112310. 15 indexed citations

12.

Oommen, Vivek, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, & George Em Karniadakis. (2022). Learning two-phase microstructure evolution using neural operators and autoencoder architectures. npj Computational Materials. 8(1). 101 indexed citations

13.

Huber, Christian, et al.. (2022). Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions. Journal of Geophysical Research Solid Earth. 127(5). 234 indexed citations breakdown →

14.

Iturrarán‐Viveros, Ursula, et al.. (2021). Machine Learning as a Seismic Prior Velocity Model Building Method for Full-Waveform Inversion: A Case Study from Colombia. Pure and Applied Geophysics. 178(2). 423–448. 15 indexed citations

15.

Shukla, Khemraj, et al.. (2020). Application of the redundant‐lifting scheme for ground‐roll attenuation in near‐surface characterization using full‐waveform inversion on P‐wave seismic data. Geophysical Prospecting. 68(7). 2078–2093. 3 indexed citations

16.

Shukla, Khemraj, et al.. (2020). Waves at a fluid-solid interface: Explicit versus implicit formulation of boundary conditions using a discontinuous Galerkin method. The Journal of the Acoustical Society of America. 147(5). 3136–3150. 10 indexed citations

17.

Shukla, Khemraj, et al.. (2017). A discontinuous Galerkin method with a modified penalty flux for broadband Biot's equation. 4080–4085. 1 indexed citations

18.

Shukla, Khemraj & Priyank Jaiswal. (2015). Amplitude preservation in multicomponent processing using local similarity. 8. 2170–2174. 1 indexed citations

19.

Shukla, Khemraj, Priyank Jaiswal, & Chandrani Singh. (2014). Recovering Uniform Coverage in a 3D Survey: Case Study from Onshore Southern India. International Journal of Geophysics. 2014. 1–9. 2 indexed citations

20.

Nath, Sankar Kumar, Khemraj Shukla, & Madhav Vyas. (2008). Seismic hazard scenario and attenuation model of the Garhwal Himalaya using near-field synthesis from weak motion seismometry. Journal of Earth System Science. 117(S2). 649–670. 21 indexed citations

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.

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