Gurtej Kanwar

1.1k total citations
29 papers, 561 citations indexed

About

Gurtej Kanwar is a scholar working on Nuclear and High Energy Physics, Condensed Matter Physics and Artificial Intelligence. According to data from OpenAlex, Gurtej Kanwar has authored 29 papers receiving a total of 561 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Nuclear and High Energy Physics, 8 papers in Condensed Matter Physics and 4 papers in Artificial Intelligence. Recurrent topics in Gurtej Kanwar's work include Particle physics theoretical and experimental studies (13 papers), Quantum Chromodynamics and Particle Interactions (13 papers) and High-Energy Particle Collisions Research (7 papers). Gurtej Kanwar is often cited by papers focused on Particle physics theoretical and experimental studies (13 papers), Quantum Chromodynamics and Particle Interactions (13 papers) and High-Energy Particle Collisions Research (7 papers). Gurtej Kanwar collaborates with scholars based in United States, Switzerland and United Kingdom. Gurtej Kanwar's co-authors include Phiala E. Shanahan, Michael S. Albergo, Danilo Jimenez Rezende, Sébastien Racanière, K. Cranmer, Denis Boyda, Daniel C. Hackett, Michael L. Wagman, William Detmold and Neill C. Warrington and has published in prestigious journals such as Physical Review Letters, ACM Transactions on Graphics and Physical review. D.

In The Last Decade

Gurtej Kanwar

25 papers receiving 549 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gurtej Kanwar United States 11 226 144 139 109 101 29 561
Michael S. Albergo United States 8 158 0.7× 139 1.0× 134 1.0× 103 0.9× 98 1.0× 14 449
Sébastien Racanière United States 11 120 0.5× 90 0.6× 154 1.1× 50 0.5× 69 0.7× 23 427
Daniel C. Hackett United States 14 490 2.2× 99 0.7× 108 0.8× 86 0.8× 68 0.7× 31 707
Phiala E. Shanahan United States 28 1.6k 7.0× 183 1.3× 184 1.3× 232 2.1× 136 1.3× 76 2.0k
Audrey Terras United States 15 77 0.3× 22 0.2× 149 1.1× 66 0.6× 166 1.6× 48 1.3k
Klas Markström Sweden 14 25 0.1× 163 1.1× 57 0.4× 72 0.7× 94 0.9× 69 585
Satoshi Morita Japan 17 58 0.3× 387 2.7× 256 1.8× 453 4.2× 46 0.5× 51 1.1k
Dmitry Savostyanov Russia 14 37 0.2× 23 0.2× 60 0.4× 127 1.2× 202 2.0× 24 648
Jordan Cotler United States 17 374 1.7× 51 0.4× 535 3.8× 528 4.8× 346 3.4× 35 1.1k
Richard Kenyon United States 25 92 0.4× 502 3.5× 61 0.4× 81 0.7× 318 3.1× 68 2.0k

Countries citing papers authored by Gurtej Kanwar

Since Specialization
Citations

This map shows the geographic impact of Gurtej Kanwar'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 Gurtej Kanwar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gurtej Kanwar more than expected).

Fields of papers citing papers by Gurtej Kanwar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gurtej Kanwar. 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 Gurtej Kanwar. The network helps show where Gurtej Kanwar may publish in the future.

Co-authorship network of co-authors of Gurtej Kanwar

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

All Works

20 of 20 papers shown
1.
2.
Boyda, Denis, Gurtej Kanwar, Fernando Romero-López, et al.. (2025). Progress in Normalizing Flows for 4d Gauge Theories. Proceedings Of Science. 66–66.
3.
Hackett, Daniel C., Denis Boyda, Gurtej Kanwar, et al.. (2024). Practical applications of machine-learned flows on gauge fields. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 11–11. 6 indexed citations
4.
Banerjee, Debasish, et al.. (2024). Broken symmetry and fractionalized flux strings in a staggered U(1) pure gauge theory. Physical review. D. 109(1). 2 indexed citations
5.
Detmold, William, William I. Jay, Gurtej Kanwar, Phiala E. Shanahan, & Michael L. Wagman. (2024). Multiparticle interpolating operators in quantum field theories with cubic symmetry. Physical review. D. 109(9). 7 indexed citations
6.
Kanwar, Gurtej, Alessandro Lovato, Noemi Rocco, & Michael L. Wagman. (2024). Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations. Physical review. C. 109(3). 3 indexed citations
7.
Abbott, Ryan, Michael S. Albergo, Denis Boyda, et al.. (2024). Multiscale Normalizing Flows for Gauge Theories. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 35–35. 3 indexed citations
8.
Kanwar, Gurtej, Constantia Alexandrou, Simone Bacchio, et al.. (2023). Pseudoscalar-pole contributions to the muon $g-2$ at the physical point. Proceedings of The 39th International Symposium on Lattice Field Theory — PoS(LATTICE2022). 306–306. 1 indexed citations
9.
Alexandrou, Constantia, Simone Bacchio, Jacob Finkenrath, et al.. (2023). η→γ*γ* transition form factor and the hadronic light-by-light η-pole contribution to the muon g−2 from lattice QCD. Physical review. D. 108(5). 9 indexed citations
10.
Cranmer, K., Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, & Phiala E. Shanahan. (2023). Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics. Nature Reviews Physics. 5(9). 526–535. 17 indexed citations
11.
Shanahan, Phiala E., Ryan Abbott, Michael S. Albergo, et al.. (2023). Sampling QCD field configurations with gauge-equivariant flow models. Proceedings of The 39th International Symposium on Lattice Field Theory — PoS(LATTICE2022). 10 indexed citations
12.
Yin, Lin, William Detmold, Gurtej Kanwar, Phiala E. Shanahan, & Michael L. Wagman. (2023). Signal-to-noise improvement through neural network contour deformations for 3D 𝑺𝑼(2) lattice gauge theory. Proceedings Of Science. 43–43.
13.
Alexandrou, Constantia, Simone Bacchio, Georg Bergner, et al.. (2023). Pion transition form factor from twisted-mass lattice QCD and the hadronic light-by-light π0-pole contribution to the muon g−2. Physical review. D. 108(9). 6 indexed citations
14.
Albergo, Michael S., Denis Boyda, K. Cranmer, et al.. (2022). Flow-based sampling in the lattice Schwinger model at criticality. Physical review. D. 106(1). 24 indexed citations
15.
Albergo, Michael S., Denis Boyda, K. Cranmer, et al.. (2022). Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions. Physical review. D. 106(7). 33 indexed citations
16.
Detmold, William, Gurtej Kanwar, Henry Lamm, Michael L. Wagman, & Neill C. Warrington. (2021). Path integral contour deformations for observables in SU(N) gauge theory. Physical review. D. 103(9). 24 indexed citations
17.
Rezende, Danilo Jimenez, George Papamakarios, Sébastien Racanière, et al.. (2020). Normalizing Flows on Tori and Spheres. International Conference on Machine Learning. 1. 8083–8092. 4 indexed citations
18.
Kanwar, Gurtej, Michael S. Albergo, Denis Boyda, et al.. (2020). Equivariant Flow-Based Sampling for Lattice Gauge Theory. Physical Review Letters. 125(12). 121601–121601. 114 indexed citations
19.
Bi, Zhen, et al.. (2020). Lattice Analysis of SU(2) with 1 Adjoint Dirac Flavor. 127–127. 6 indexed citations
20.
Albergo, Michael S., Gurtej Kanwar, & Phiala E. Shanahan. (2019). Flow-based generative models for Markov chain Monte Carlo in lattice field theory. Physical review. D. 100(3). 132 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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