István Gyöngy

3.7k total citations
64 papers, 2.2k citations indexed

About

István Gyöngy is a scholar working on Finance, Computational Theory and Mathematics and Mathematical Physics. According to data from OpenAlex, István Gyöngy has authored 64 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Finance, 22 papers in Computational Theory and Mathematics and 21 papers in Mathematical Physics. Recurrent topics in István Gyöngy's work include Stochastic processes and financial applications (50 papers), Advanced Mathematical Modeling in Engineering (22 papers) and Differential Equations and Numerical Methods (11 papers). István Gyöngy is often cited by papers focused on Stochastic processes and financial applications (50 papers), Advanced Mathematical Modeling in Engineering (22 papers) and Differential Equations and Numerical Methods (11 papers). István Gyöngy collaborates with scholars based in United Kingdom, Hungary and United States. István Gyöngy's co-authors include Н. В. Крылов, Teresa Martı́nez, David Nualart, David Nualart, Étienne Pardoux, Miklós Rásonyi, Carles Rovira, Sotirios Sabanis, V. Bally and David Šiška and has published in prestigious journals such as Nature, Mathematics of Computation and Journal of Differential Equations.

In The Last Decade

István Gyöngy

59 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
István Gyöngy United Kingdom 24 1.7k 609 531 380 364 64 2.2k
David Nualart United States 26 2.9k 1.7× 506 0.8× 1.4k 2.6× 657 1.7× 298 0.8× 120 3.6k
Jan Seidler Czechia 14 1.3k 0.7× 317 0.5× 386 0.7× 270 0.7× 326 0.9× 29 2.0k
Peter K. Friz Germany 24 1.4k 0.8× 340 0.6× 705 1.3× 356 0.9× 188 0.5× 87 2.0k
Yaozhong Hu United States 30 3.0k 1.7× 296 0.5× 908 1.7× 545 1.4× 337 0.9× 157 3.9k
Hiroshi Kunita Japan 22 1.1k 0.7× 364 0.6× 709 1.3× 478 1.3× 340 0.9× 49 2.2k
François Delarue France 27 1.8k 1.0× 200 0.3× 484 0.9× 424 1.1× 198 0.5× 60 2.4k
Guiseppe Da Prato Italy 5 1.5k 0.9× 854 1.4× 623 1.2× 992 2.6× 1.1k 3.0× 6 2.5k
Shanjian Tang China 20 1.5k 0.9× 287 0.5× 153 0.3× 228 0.6× 360 1.0× 79 1.7k
Yves Achdou France 23 583 0.3× 528 0.9× 238 0.4× 297 0.8× 141 0.4× 82 2.0k
Jaime San Martı́n Chile 18 692 0.4× 245 0.4× 455 0.9× 216 0.6× 129 0.4× 84 1.4k

Countries citing papers authored by István Gyöngy

Since Specialization
Citations

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

Fields of papers citing papers by István Gyöngy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of István Gyöngy

This figure shows the co-authorship network connecting the top 25 collaborators of István Gyöngy. A scholar is included among the top collaborators of István Gyöngy 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 István Gyöngy. István Gyöngy 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.
Gyöngy, István & Seick Kim. (2024). Harnack inequality for parabolic equations in double-divergence form with singular lower order coefficients. Journal of Differential Equations. 412. 857–880. 2 indexed citations
2.
Davie, A. M., et al.. (2023). On partially observed jump diffusions II: the filtering density. Stochastic Partial Differential Equations Analysis and Computations. 12(3). 1628–1698. 1 indexed citations
3.
Gyöngy, István, et al.. (2020). On $$L_p$$-solvability of stochastic integro-differential equations. Stochastic Partial Differential Equations Analysis and Computations. 9(2). 295–342. 3 indexed citations
4.
Gyöngy, István, et al.. (2016). Localization errors in solving stochastic partial differential equations in the whole space. Mathematics of Computation. 86(307). 2373–2397. 3 indexed citations
5.
Gyöngy, István, Sotirios Sabanis, & David Šiška. (2015). Convergence of tamed Euler schemes for a class of stochastic evolution equations. Stochastic Partial Differential Equations Analysis and Computations. 4(2). 225–245. 22 indexed citations
6.
Gyöngy, István & Miklós Rásonyi. (2011). A note on Euler approximations for SDEs with Hölder continuous diffusion coefficients. Stochastic Processes and their Applications. 121(10). 2189–2200. 77 indexed citations
7.
Gyöngy, István & David Šiška. (2009). On Finite-Difference Approximations for Normalized Bellman Equations. Applied Mathematics & Optimization. 60(3). 297–339. 5 indexed citations
8.
Gyöngy, István & Н. В. Крылов. (2009). Higher Order Derivative Estimates for Finite-difference Schemes for Linear Elliptic and Parabolic Equations. Methods and Applications of Analysis. 16(2). 187–216. 3 indexed citations
9.
Gyöngy, István, et al.. (2006). Rate of Convergence of Wong–Zakai Approximations for Stochastic Partial Differential Equations. Applied Mathematics & Optimization. 54(3). 341–341. 32 indexed citations
10.
Gyöngy, István & Н. В. Крылов. (2003). On the splitting-up method and stochastic partial differential equations. The Annals of Probability. 31(2). 74 indexed citations
11.
Gyöngy, István & Teresa Martı́nez. (2001). On Stochastic Differential Equations with Locally Unbounded Drift. Czechoslovak Mathematical Journal. 51(4). 763–783. 67 indexed citations
12.
Gyöngy, István & Carles Rovira. (2000). On Lp-solutions of semilinear stochastic partial differential equations. Stochastic Processes and their Applications. 90(1). 83–108. 46 indexed citations
13.
Gyöngy, István. (1998). A note on Euler's Approximations. Potential Analysis. 8(3). 205–216. 89 indexed citations
14.
Gyöngy, István. (1998). Existence and uniqueness results for semilinear stochastic partial differential equations. Stochastic Processes and their Applications. 73(2). 271–299. 110 indexed citations
15.
Gyöngy, István & David Nualart. (1995). Implicit scheme for quasi-linear parabolic partial differential equations perturbed by space-time white noise. Stochastic Processes and their Applications. 58(1). 57–72. 37 indexed citations
16.
Bally, V., István Gyöngy, & Étienne Pardoux. (1994). White Noise Driven Parabolic SPDEs with Measurable Drift. Journal of Functional Analysis. 120(2). 484–510. 42 indexed citations
17.
Gyöngy, István & Étienne Pardoux. (1993). On quasi-linear stochastic partial differential equations. Probability Theory and Related Fields. 94(4). 413–425. 54 indexed citations
18.
Gyöngy, István & Н. В. Крылов. (1992). Stochastic partial differential equations with unbounded coefficients and applications. III. Stochastics and stochastics reports. 40(1-2). 77–115. 16 indexed citations
19.
Gyöngy, István, et al.. (1990). On the approximation of stochastic differential equation and on stroock-varadhan's support theorem. Computers & Mathematics with Applications. 19(1). 65–70. 23 indexed citations
20.
Gyöngy, István. (1989). On the approximation of stochastic partial differential equations II. Stochastics and stochastics reports. 26(3). 129–164. 19 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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