Adil Rasheed

4.8k total citations · 1 hit paper
144 papers, 3.0k citations indexed

About

Adil Rasheed is a scholar working on Computational Mechanics, Environmental Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, Adil Rasheed has authored 144 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Computational Mechanics, 40 papers in Environmental Engineering and 35 papers in Statistical and Nonlinear Physics. Recurrent topics in Adil Rasheed's work include Model Reduction and Neural Networks (34 papers), Wind and Air Flow Studies (27 papers) and Fluid Dynamics and Vibration Analysis (26 papers). Adil Rasheed is often cited by papers focused on Model Reduction and Neural Networks (34 papers), Wind and Air Flow Studies (27 papers) and Fluid Dynamics and Vibration Analysis (26 papers). Adil Rasheed collaborates with scholars based in Norway, United States and Netherlands. Adil Rasheed's co-authors include Omer San, Trond Kvamsdal, Suraj Pawar, Mandar Tabib, M. Salman Siddiqui, Sk. Mashfiqur Rahman, Prakash Vedula, Darren Robinson, Frédéric Haldi and Shady E. Ahmed and has published in prestigious journals such as PLoS ONE, Scientific Reports and Applied Energy.

In The Last Decade

Adil Rasheed

132 papers receiving 2.9k citations

Hit Papers

Digital Twin: Values, Challenges and Enablers From a Mode... 2020 2026 2022 2024 2020 250 500 750 1000
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.

  1. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
    2020 1.0k citations Adil Rasheed, Omer San et al. IEEE Access profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Adil Rasheed Norway 24 660 657 583 550 473 144 3.0k
Omer San United States 30 1.2k 1.9× 650 1.0× 233 0.4× 1.1k 2.1× 344 0.7× 125 3.4k
Trond Kvamsdal Norway 24 1.3k 2.0× 613 0.9× 375 0.6× 237 0.4× 386 0.8× 85 3.1k
András Sóbester United Kingdom 17 586 0.9× 242 0.4× 287 0.5× 355 0.6× 814 1.7× 75 4.3k
Tushar Goel United States 19 447 0.7× 175 0.3× 196 0.3× 236 0.4× 859 1.8× 54 3.6k
Eleni Chatzi Switzerland 44 464 0.7× 93 0.1× 331 0.6× 362 0.7× 301 0.6× 333 6.6k
Zhen Hu United States 35 194 0.3× 441 0.7× 269 0.5× 136 0.2× 266 0.6× 228 4.6k
Shiji Song China 37 213 0.3× 1.2k 1.8× 242 0.4× 349 0.6× 269 0.6× 196 6.3k
Yao Zheng China 26 1.1k 1.7× 110 0.2× 132 0.2× 270 0.5× 681 1.4× 257 2.9k
Manolis Papadrakakis Greece 44 840 1.3× 90 0.1× 384 0.7× 211 0.4× 133 0.3× 212 6.9k
Jaroslaw Sobieszczanski‐Sobieski United States 38 545 0.8× 862 1.3× 118 0.2× 130 0.2× 1.2k 2.5× 127 6.1k

Countries citing papers authored by Adil Rasheed

Since Specialization
Citations

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

Fields of papers citing papers by Adil Rasheed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Adil Rasheed

This figure shows the co-authorship network connecting the top 25 collaborators of Adil Rasheed. A scholar is included among the top collaborators of Adil Rasheed 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 Adil Rasheed. Adil Rasheed 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.
Rasheed, Adil, et al.. (2025). Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control. Scientific Reports. 15(1). 9344–9344. 1 indexed citations
2.
Rasheed, Adil, et al.. (2025). ChronoSHAP: Revealing temporal patterns from transformers and LTSF-linear models in time series forecasting. Knowledge-Based Systems. 331. 114802–114802.
3.
Rasheed, Adil, et al.. (2024). Physics-guided federated learning as an enabler for digital twins. Expert Systems with Applications. 258. 125169–125169. 4 indexed citations
4.
Rasheed, Adil, et al.. (2024). Multi-Target Tracking for Autonomous Surface Vessels Using LiDAR and AIS Data Integration. Applied Ocean Research. 154. 104348–104348. 2 indexed citations
5.
Rasheed, Adil, et al.. (2024). Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energy. 3 indexed citations
6.
Rasheed, Adil, Mandar Tabib, Kjetil Johannessen, et al.. (2024). Digital Twin for Wind Energy: Latest Updates From the NorthWind Project. 1 indexed citations
7.
Rasheed, Adil, et al.. (2024). Self-Supervised Modular Architecture for Multi-Sensor Anomaly Detection and Localization. 1278–1283. 4 indexed citations
8.
Rasheed, Adil, et al.. (2024). Multivariate Time Series Anomaly Detection via Low-Rank and Sparse Decomposition. IEEE Sensors Journal. 24(21). 34942–34952. 8 indexed citations
9.
Rasheed, Adil, et al.. (2023). Standalone, Descriptive, and Predictive Digital Twin of an Onshore Wind Farm in Complex Terrain. Journal of Physics Conference Series. 2626(1). 12030–12030. 3 indexed citations
10.
11.
Rasheed, Adil, et al.. (2023). Deep learning assisted physics-based modeling of aluminum extraction process. Engineering Applications of Artificial Intelligence. 125. 106623–106623. 3 indexed citations
12.
Rasheed, Adil, et al.. (2023). Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality. IEEE Access. 11. 35035–35058. 17 indexed citations
13.
Pawar, Suraj, et al.. (2022). Physics guided neural networks for modelling of non-linear dynamics. Neural Networks. 154. 333–345. 46 indexed citations
14.
Rasheed, Adil, et al.. (2022). Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach. Applied Soft Computing. 128. 109533–109533. 32 indexed citations
15.
Rasheed, Adil, et al.. (2021). A novel hybrid analysis and modeling approach applied to aluminum electrolysis process. Journal of Process Control. 105. 62–77. 20 indexed citations
16.
Taskar, Bhushan, et al.. (2021). Statistical modeling of Ship’s hydrodynamic performance indicator. Applied Ocean Research. 111. 102623–102623. 13 indexed citations
17.
Rasheed, Adil, et al.. (2020). Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data. Duo Research Archive (University of Oslo). 54 indexed citations
18.
Rasheed, Adil, et al.. (2020). Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance Using Deep Reinforcement Learning. IEEE Access. 8. 41466–41481. 57 indexed citations
19.
Rasheed, Adil, Omer San, & Trond Kvamsdal. (2020). Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access. 8. 21980–22012. 1025 indexed citations breakdown →
20.
Ahmed, Shady E., Sk. Mashfiqur Rahman, Omer San, Adil Rasheed, & I. M. Navon. (2019). Memory embedded non-intrusive reduced order modeling of non-ergodic flows. Physics of Fluids. 31(12). 37 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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