Anirvan M. Sengupta

5.8k citations
204 papers · 3.8k indexed · h-index 28

Anirvan M. Sengupta

189 papers receiving 3.7k citations

Peers

Anirvan M. Sengupta
Comparison fields: 5 of 149
  • Condensed Matter Physics 449
  • Nuclear and High Energy Physics 481
  • Acoustics and Ultrasonics 31
  • Statistical and Nonlinear Physics 361
  • Modeling and Simulation 106
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Countries citing papers authored by Anirvan M. Sengupta

Since Specialization
Citations

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

Fields of papers citing papers by Anirvan M. Sengupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anirvan M. Sengupta. 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 Anirvan M. Sengupta. The network helps show where Anirvan M. Sengupta may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Anirvan M. Sengupta, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Anirvan M. Sengupta Line = papers co-authored together Anirvan M. Sengupta links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 202319
3 20221
4
A simple normative network approximates local non-Hebbian learning in the cortex
20201
5
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
20193
6 20192
7
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling
20182
8 20151
9 201416
10 20140
11 20143
12 20141
13 201173
14 200932
15 200963
16 200815
17 200514
18 200410
19 2003160
20
A new approach to relief valve load calculations.
19783

About Anirvan M. Sengupta

Anirvan M. Sengupta is a scholar working on Filtration and Separation, Electrical and Electronic Engineering and Control and Systems Engineering, having authored 204 papers that have together received 3.8k indexed citations. Recurring topics across this work include Electric Motor Design and Analysis (37 papers), Magnetic Bearings and Levitation Dynamics (30 papers), Advanced DC-DC Converters (28 papers), Silicon Carbide Semiconductor Technologies (26 papers), Induction Heating and Inverter Technology (23 papers), Multilevel Inverters and Converters (19 papers), Genomics and Chromatin Dynamics (17 papers) and Magnetic Properties and Applications (15 papers). The work is most often cited by research in Condensed Matter Physics (449 citations), Nuclear and High Energy Physics (481 citations) and Acoustics and Ultrasonics (31 citations). Anirvan M. Sengupta has collaborated with scholars based in India, United States and Germany. Frequent co-authors include Antoine Georges, Steven H. Simon, Aris L. Moustakas, Gautam Mandal, Martin A. Nowak, Boris I. Shraiman, Natalia L. Komarova, Marko Djordjević, Prasad V. Jallepalli and Christoph Lengauer. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Physical Review Letters.

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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