Jonas Šukys

432 total citations
17 papers, 184 citations indexed

About

Jonas Šukys is a scholar working on Statistics, Probability and Uncertainty, Environmental Engineering and Water Science and Technology. According to data from OpenAlex, Jonas Šukys has authored 17 papers receiving a total of 184 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Statistics, Probability and Uncertainty, 7 papers in Environmental Engineering and 5 papers in Water Science and Technology. Recurrent topics in Jonas Šukys's work include Probabilistic and Robust Engineering Design (10 papers), Hydrology and Watershed Management Studies (5 papers) and Groundwater flow and contamination studies (4 papers). Jonas Šukys is often cited by papers focused on Probabilistic and Robust Engineering Design (10 papers), Hydrology and Watershed Management Studies (5 papers) and Groundwater flow and contamination studies (4 papers). Jonas Šukys collaborates with scholars based in Switzerland, United States and Spain. Jonas Šukys's co-authors include Siddhartha Mishra, Ch. Schwab, Gian-Duri Lieberherr, Philip Chu, Damien Bouffard, Stefan Wunderle, Alfred Wüest, Christoph Schwab, Manuel J. Castro and Carlos Sánchez‐Linares and has published in prestigious journals such as Journal of Computational Physics, Journal of Hydrology and SIAM Journal on Scientific Computing.

In The Last Decade

Jonas Šukys

17 papers receiving 172 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonas Šukys Switzerland 5 98 60 49 24 23 17 184
Justin Winokur United States 8 122 1.2× 16 0.3× 42 0.9× 36 1.5× 69 3.0× 17 236
Imad Elmahi France 9 4 0.0× 172 2.9× 21 0.4× 41 1.7× 24 1.0× 30 293
Simone Marras United States 11 5 0.1× 164 2.7× 29 0.6× 38 1.6× 28 1.2× 27 298
Abdelaziz Beljadid Canada 11 2 0.0× 99 1.6× 95 1.9× 36 1.5× 16 0.7× 32 277
Mihai Alexe United States 6 10 0.1× 23 0.4× 21 0.4× 136 5.7× 7 0.3× 13 208
Marc de la Asunción Spain 11 9 0.1× 151 2.5× 7 0.1× 50 2.1× 16 0.7× 24 375
Selime Gürol France 10 7 0.1× 20 0.3× 32 0.7× 151 6.3× 59 2.6× 19 220
Yuri N. Skiba Mexico 12 3 0.0× 116 1.9× 55 1.1× 76 3.2× 42 1.8× 60 325
Nashat N. Ahmad United States 11 6 0.1× 335 5.6× 149 3.0× 119 5.0× 16 0.7× 50 533
Santha Akella United States 10 8 0.1× 26 0.4× 42 0.9× 196 8.2× 186 8.1× 25 351

Countries citing papers authored by Jonas Šukys

Since Specialization
Citations

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

Fields of papers citing papers by Jonas Šukys

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonas Šukys

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

All Works

17 of 17 papers shown
1.
Šukys, Jonas, et al.. (2023). A comparison of numerical approaches for statistical inference with stochastic models. Stochastic Environmental Research and Risk Assessment. 37(8). 3041–3061. 3 indexed citations
2.
Bouffard, Damien, et al.. (2022). A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1. Geoscientific model development. 15(20). 7715–7730. 2 indexed citations
3.
Fenicia, Fabrizio, et al.. (2022). Application of stochastic time dependent parameters to improve the characterization of uncertainty in conceptual hydrological models. Journal of Hydrology. 612. 128057–128057. 3 indexed citations
4.
Chu, Philip, Jonas Šukys, Gian-Duri Lieberherr, et al.. (2020). Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4. Geoscientific model development. 13(3). 1267–1284. 35 indexed citations
5.
Šukys, Jonas, et al.. (2020). SPUX - a Scalable Package for Bayesian Uncertainty Quantification. 3 indexed citations
6.
Chu, Philip, Jonas Šukys, Gian-Duri Lieberherr, et al.. (2019). Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models. 4 indexed citations
7.
Barth, Timothy J. & Jonas Šukys. (2019). On the Calculation of Exact Cumulative Distribution Statistics for Burgers Equation. NASA STI Repository (National Aeronautics and Space Administration). 1 indexed citations
8.
Šukys, Jonas, et al.. (2018). Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation. SIAM Journal on Scientific Computing. 40(5). B1361–B1390. 2 indexed citations
9.
Mishra, Siddhartha, Ch. Schwab, & Jonas Šukys. (2016). Multi-level Monte Carlo finite volume methods for uncertainty quantification of acoustic wave propagation in random heterogeneous layered medium. Journal of Computational Physics. 312. 192–217. 19 indexed citations
10.
Sánchez‐Linares, Carlos, Marc de la Asunción, Manuel J. Castro, Siddhartha Mishra, & Jonas Šukys. (2015). Multi-level Monte Carlo finite volume method for shallow water equations with uncertain parameters applied to landslides-generated tsunamis. Applied Mathematical Modelling. 39(23-24). 7211–7226. 10 indexed citations
11.
Mishra, Siddhartha, Christoph Schwab, & Jonas Šukys. (2014). Multi-Level Monte Carlo Finite Volume methods for uncertainty quantification of acoustic wave propagation in random heterogeneous layered medium. Repository for Publications and Research Data (ETH Zurich). 1 indexed citations
12.
Mishra, Siddhartha, Ch. Schwab, & Jonas Šukys. (2012). Multi-level Monte Carlo finite volume methods for nonlinear systems of conservation laws in multi-dimensions. Journal of Computational Physics. 231(8). 3365–3388. 66 indexed citations
13.
Mishra, Siddhartha, Ch. Schwab, & Jonas Šukys. (2012). Multilevel Monte Carlo Finite Volume Methods for Shallow Water Equations with Uncertain Topography in Multi-dimensions. SIAM Journal on Scientific Computing. 34(6). B761–B784. 28 indexed citations
14.
Mishra, Siddhartha, Christoph Schwab, & Jonas Šukys. (2012). Multi-level Monte Carlo finite volume methods for uncertainty quantification in nonlinear systems of balance laws. Repository for Publications and Research Data (ETH Zurich). 225–294. 2 indexed citations
15.
Šukys, Jonas, Siddhartha Mishra, & Christoph Schwab. (2011). Static load balancing for multi-level Monte Carlo finite volume solvers. Repository for Publications and Research Data (ETH Zurich). 1 indexed citations
16.
Mishra, Siddhartha, Christoph Schwab, & Jonas Šukys. (2011). Multi-level Monte Carlo finite volume methods for nonlinear systems of conservation laws in multi-dimensions. Repository for Publications and Research Data (ETH Zurich). 1 indexed citations
17.
Mishra, Siddhartha, Christoph Schwab, & Jonas Šukys. (2011). Multi-level Monte Carlo finite volume methods for shallow water equations with uncertain topography in multi-dimensions. Repository for Publications and Research Data (ETH Zurich). 3 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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