Panos Stinis

823 citations
43 papers · 400 · h-index 12

Impact in

Papers in

Panos Stinis

41 papers receiving 382 citations

Peers

Panos Stinis
Comparison fields: 5 of 63
  • Statistical and Nonlinear Physics 158
  • Statistics, Probability and Uncertainty 55
  • Automotive Engineering 57
  • Artificial Intelligence 97
  • Computational Mechanics 61
Replace Roberto Molinaro with:
Roberto Molinaro Switzerland
Jian Cheng Wong Singapore
J. Nathan Kutz United States
Kadierdan Kaheman United States
Kookjin Lee United States
J. D. Rodríguez Spain
Lei Nie China
George Em Karniadakis United States
Maria Emelianenko United States
Panos Stinis relative to Roberto Molinaro Switzerland Roberto Molinaro's profile →
Citations per field
00.5×4.5×
Roberto Molinaro · 1×
Citations per year

Countries citing papers authored by Panos Stinis

Since Specialization
Citations

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

Fields of papers citing papers by Panos Stinis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Panos Stinis, 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 Panos Stinis Line = papers co-authored together Panos Stinis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202342
2 202232
3 202432
4 201132
5 201523
6 202318
7 201918
8 202417
9 202215
10 202312
11 202311
12 201811
13 202410
14 202310
15 202410
16 20179
17 20089
18 20247
19 20117
20 20246

About Panos Stinis

Panos Stinis is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Electrical and Electronic Engineering, Statistics, Probability and Uncertainty and Computational Mechanics, having authored 43 papers that have together received 400 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (18 papers), Probabilistic and Robust Engineering Design (9 papers), Advanced Battery Technologies Research (6 papers), Advanced battery technologies research (6 papers), Nuclear Engineering Thermal-Hydraulics (5 papers), Gaussian Processes and Bayesian Inference (5 papers), Fluid Dynamics and Turbulent Flows (4 papers) and Target Tracking and Data Fusion in Sensor Networks (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (158 citations), Statistics, Probability and Uncertainty (55 citations), Automotive Engineering (57 citations), Artificial Intelligence (97 citations) and Computational Mechanics (61 citations). Panos Stinis has collaborated with scholars based in United States, Israel and Morocco. Frequent co-authors include Alexandre M. Tartakovsky, George Em Karniadakis, Qizhi He, Vasileios Maroulas, Yucheng Fu, Mauro Perego, Shady E. Ahmed, Jing Li, Dan Givoli and Eli Turkel. Their work appears in journals such as Journal of Computational Physics, Machine Learning Science and Technology, Journal of Power Sources, Computer Methods in Applied Mechanics and Engineering and Journal of Advances in Modeling Earth Systems.

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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