Abhinav Narasingam

918 total citations
19 papers, 716 citations indexed

About

Abhinav Narasingam is a scholar working on Statistical and Nonlinear Physics, Control and Systems Engineering and Mechanical Engineering. According to data from OpenAlex, Abhinav Narasingam has authored 19 papers receiving a total of 716 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistical and Nonlinear Physics, 8 papers in Control and Systems Engineering and 7 papers in Mechanical Engineering. Recurrent topics in Abhinav Narasingam's work include Model Reduction and Neural Networks (16 papers), Control Systems and Identification (7 papers) and Probabilistic and Robust Engineering Design (6 papers). Abhinav Narasingam is often cited by papers focused on Model Reduction and Neural Networks (16 papers), Control Systems and Identification (7 papers) and Probabilistic and Robust Engineering Design (6 papers). Abhinav Narasingam collaborates with scholars based in United States and South Korea. Abhinav Narasingam's co-authors include Joseph Sang‐Il Kwon, Prashanth Siddhamshetty, Bhavana Bhadriraju, Mohammed Saad Faizan Bangi, Sang Hwan Son and Eduardo Gildin and has published in prestigious journals such as Industrial & Engineering Chemistry Research, AIChE Journal and Computers & Chemical Engineering.

In The Last Decade

Abhinav Narasingam

19 papers receiving 699 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Abhinav Narasingam United States 12 339 276 210 138 134 19 716
Jian Deng Canada 16 191 0.6× 62 0.2× 103 0.5× 34 0.2× 103 0.8× 79 744
Zhiqiang Wu China 11 124 0.4× 98 0.4× 86 0.4× 24 0.2× 48 0.4× 111 469
P. Astrid Netherlands 10 131 0.4× 415 1.5× 158 0.8× 120 0.9× 186 1.4× 17 619
Ton Backx Netherlands 9 258 0.8× 299 1.1× 61 0.3× 9 0.1× 149 1.1× 26 596
Yan Yong United States 12 94 0.3× 79 0.3× 65 0.3× 40 0.3× 110 0.8× 45 524
Guo‐Kang Er Macao 16 103 0.3× 346 1.3× 54 0.3× 60 0.4× 511 3.8× 65 744
Antoine Blanchard United States 13 167 0.5× 64 0.2× 46 0.2× 23 0.2× 34 0.3× 24 411
Cheng Huang United States 15 221 0.7× 256 0.9× 40 0.2× 16 0.1× 118 0.9× 66 1.1k
Liming W. Salvino United States 10 101 0.3× 76 0.3× 108 0.5× 26 0.2× 31 0.2× 28 495

Countries citing papers authored by Abhinav Narasingam

Since Specialization
Citations

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

Fields of papers citing papers by Abhinav Narasingam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abhinav Narasingam

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

All Works

19 of 19 papers shown
1.
2.
Son, Sang Hwan, Abhinav Narasingam, & Joseph Sang‐Il Kwon. (2021). Integration of offset-free control framework with Koopman Lyapunov-based model predictive control. 2818–2823. 2 indexed citations
3.
Narasingam, Abhinav, Sang Hwan Son, & Joseph Sang‐Il Kwon. (2021). Data-driven feedback stabilisation of nonlinear systems: Koopman-based model predictive control. International Journal of Control. 96(3). 770–781. 30 indexed citations
4.
Narasingam, Abhinav. (2020). Operator Theoretic Model Predictive Control of Moving Boundary Dynamical Systems: Application to Hydraulic Fracturing. OakTrust (Texas A&M University Libraries). 1 indexed citations
5.
Narasingam, Abhinav & Joseph Sang‐Il Kwon. (2020). Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation. Journal of Process Control. 91. 25–36. 44 indexed citations
6.
Narasingam, Abhinav & Joseph Sang‐Il Kwon. (2020). Closed-loop stabilization of nonlinear systems using Koopman Lyapunov-based model predictive control. 704–709. 7 indexed citations
7.
Bhadriraju, Bhavana, Mohammed Saad Faizan Bangi, Abhinav Narasingam, & Joseph Sang‐Il Kwon. (2020). Operable adaptive sparse identification of systems: Application to chemical processes. AIChE Journal. 66(11). 72 indexed citations
8.
Narasingam, Abhinav & Joseph Sang‐Il Kwon. (2020). Koopman operator-based model identification and control of hydraulic fracture propagation. 4533–4538. 1 indexed citations
9.
Gildin, Eduardo, et al.. (2019). Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods. Fluids. 4(3). 138–138. 10 indexed citations
10.
Bangi, Mohammed Saad Faizan, Abhinav Narasingam, Prashanth Siddhamshetty, & Joseph Sang‐Il Kwon. (2019). Enlarging the Domain of Attraction of the Local Dynamic Mode Decomposition with Control Technique: Application to Hydraulic Fracturing. Industrial & Engineering Chemistry Research. 58(14). 5588–5601. 19 indexed citations
11.
Narasingam, Abhinav & Joseph Sang‐Il Kwon. (2019). Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems. AIChE Journal. 65(11). 86 indexed citations
12.
Bhadriraju, Bhavana, Abhinav Narasingam, & Joseph Sang‐Il Kwon. (2019). Machine learning-based adaptive model identification of systems: Application to a chemical process. Process Safety and Environmental Protection. 152. 372–383. 74 indexed citations
13.
Narasingam, Abhinav, et al.. (2018). Model order reduction of nonlinear parabolic PDE systems with moving boundaries using sparse proper orthogonal decomposition: Application to hydraulic fracturing. Computers & Chemical Engineering. 112. 92–100. 48 indexed citations
14.
Narasingam, Abhinav & Joseph Sang‐Il Kwon. (2018). Data-driven identification of interpretable reduced-order models using sparse regression. Computers & Chemical Engineering. 119. 101–111. 63 indexed citations
15.
Narasingam, Abhinav, Prashanth Siddhamshetty, & Joseph Sang‐Il Kwon. (2018). Handling Spatial Heterogeneity in Reservoir Parameters Using Proper Orthogonal Decomposition Based Ensemble Kalman Filter for Model-Based Feedback Control of Hydraulic Fracturing. Industrial & Engineering Chemistry Research. 57(11). 3977–3989. 54 indexed citations
16.
17.
Narasingam, Abhinav, Prashanth Siddhamshetty, & Joseph Sang‐Il Kwon. (2018). Identification of spatially varying geological properties in a heterogeneous reservoir using EnKF and POD based parameterization. 1144–1149. 1 indexed citations
18.
Narasingam, Abhinav & Joseph Sang‐Il Kwon. (2017). Development of local dynamic mode decomposition with control: Application to model predictive control of hydraulic fracturing. Computers & Chemical Engineering. 106. 501–511. 95 indexed citations
19.
Narasingam, Abhinav, Prashanth Siddhamshetty, & Joseph Sang‐Il Kwon. (2017). Temporal clustering for order reduction of nonlinear parabolic PDE systems with time‐dependent spatial domains: Application to a hydraulic fracturing process. AIChE Journal. 63(9). 3818–3831. 77 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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