Panos Stinis

823 total citations
43 papers, 400 citations indexed

About

Panos Stinis is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Panos Stinis has authored 43 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Statistical and Nonlinear Physics, 11 papers in Artificial Intelligence and 9 papers in Electrical and Electronic Engineering. Recurrent topics in Panos Stinis's work include Model Reduction and Neural Networks (18 papers), Probabilistic and Robust Engineering Design (9 papers) and Advanced Battery Technologies Research (6 papers). Panos Stinis is often cited by papers focused on Model Reduction and Neural Networks (18 papers), Probabilistic and Robust Engineering Design (9 papers) and Advanced Battery Technologies Research (6 papers). Panos Stinis collaborates with scholars based in United States, Israel and Morocco. Panos Stinis's co-authors include Alexandre M. Tartakovsky, George Em Karniadakis, Qizhi He, Vasileios Maroulas, Yucheng Fu, Mauro Perego, Shady E. Ahmed, Jing Li, Eli Turkel and Dan Givoli and has published in prestigious journals such as Journal of Power Sources, Scientific Reports and Journal of Computational Physics.

In The Last Decade

Panos Stinis

41 papers receiving 382 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Panos Stinis United States 12 158 97 92 61 57 43 400
Jian Cheng Wong Singapore 9 230 1.5× 50 0.5× 41 0.4× 105 1.7× 20 0.4× 22 430
J. Nathan Kutz United States 2 115 0.7× 71 0.7× 40 0.4× 59 1.0× 10 0.2× 2 352
Roberto Molinaro Switzerland 9 347 2.2× 103 1.1× 46 0.5× 177 2.9× 13 0.2× 10 539
Liang Deng China 11 135 0.9× 75 0.8× 36 0.4× 176 2.9× 14 0.2× 55 440
Kadierdan Kaheman United States 4 205 1.3× 88 0.9× 22 0.2× 44 0.7× 6 0.1× 6 332
Kookjin Lee United States 9 302 1.9× 72 0.7× 48 0.5× 161 2.6× 3 0.1× 26 462
Lei Nie China 14 103 0.7× 25 0.3× 130 1.4× 33 0.5× 11 0.2× 83 542
Vivek Oommen United States 6 137 0.9× 45 0.5× 24 0.3× 80 1.3× 4 0.1× 10 296
Markus Abel Australia 7 77 0.5× 61 0.6× 15 0.2× 27 0.4× 8 0.1× 14 245

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-authorship network of co-authors of Panos Stinis

This figure shows the co-authorship network connecting the top 25 collaborators of Panos Stinis. A scholar is included among the top collaborators of Panos Stinis 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 Panos Stinis. Panos Stinis 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.
Stinis, Panos, et al.. (2025). Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. Journal of Computational Physics. 542. 114226–114226. 4 indexed citations
2.
Stinis, Panos, et al.. (2025). SPIKANs: separable physics-informed Kolmogorov–Arnold networks. Machine Learning Science and Technology. 6(3). 35060–35060. 4 indexed citations
3.
Bao, Jie, Yunxiang Chen, Yucheng Fu, et al.. (2025). Computationally efficient models for aqueous organic redox flow batteries. Journal of Energy Storage. 134. 118134–118134.
4.
Karniadakis, George Em, et al.. (2024). SMS: Spiking marching scheme for efficient long time integration of differential equations. Journal of Computational Physics. 516. 113363–113363. 2 indexed citations
5.
Stinis, Panos, et al.. (2024). Physics-informed machine learning of the correlation functions in bulk fluids. Physics of Fluids. 36(1). 7 indexed citations
6.
Fu, Yucheng, et al.. (2024). A multifidelity approach to continual learning for physical systems. Machine Learning Science and Technology. 5(2). 25042–25042. 10 indexed citations
7.
Stinis, Panos, et al.. (2024). ViTO: Vision Transformer-Operator. Computer Methods in Applied Mechanics and Engineering. 428. 117109–117109. 32 indexed citations
8.
Ahmed, Shady E., et al.. (2024). Stacked networks improve physics-informed training: Applications to neural networks and deep operator networks. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 7(1). 134–162. 6 indexed citations
9.
Prange, Micah P., et al.. (2024). Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS. The Journal of Physical Chemistry A. 129(1). 346–355. 3 indexed citations
10.
Kim, Youngeun, et al.. (2024). Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding. Frontiers in Neuroscience. 18. 1346805–1346805. 3 indexed citations
11.
Stinis, Panos, Constantinos Daskalakis, & Paul J. Atzberger. (2024). SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics. Journal of Computational Physics. 519. 113442–113442.
12.
Perego, Mauro, et al.. (2023). Multifidelity deep operator networks for data-driven and physics-informed problems. Journal of Computational Physics. 493. 112462–112462. 42 indexed citations
13.
Chen, Wenqian & Panos Stinis. (2023). Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. Journal of Computational Physics. 498. 112683–112683. 5 indexed citations
14.
Ahmed, Shady E. & Panos Stinis. (2023). A multifidelity deep operator network approach to closure for multiscale systems. Computer Methods in Applied Mechanics and Engineering. 414. 116161–116161. 11 indexed citations
15.
Dong, Justin, et al.. (2023). Machine learning methods for particle stress development in suspension Poiseuille flows. Rheologica Acta. 62(10). 507–534. 5 indexed citations
16.
He, Qizhi, Panos Stinis, & Alexandre M. Tartakovsky. (2022). Physics-constrained deep neural network method for estimating parameters in a redox flow battery. Journal of Power Sources. 528. 231147–231147. 32 indexed citations
17.
He, Qizhi, Yucheng Fu, Panos Stinis, & Alexandre M. Tartakovsky. (2022). Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. Journal of Power Sources. 542. 231807–231807. 15 indexed citations
18.
Wan, Hui, Carol S. Woodward, Shixuan Zhang, et al.. (2020). Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling. Journal of Advances in Modeling Earth Systems. 12(10). 5 indexed citations
19.
20.
Stinis, Panos. (2019). Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning.. National Conference on Artificial Intelligence. 1 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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