Ivan Yotov

4.9k total citations
82 papers, 3.5k citations indexed

About

Ivan Yotov is a scholar working on Computational Mechanics, Computational Theory and Mathematics and Mechanics of Materials. According to data from OpenAlex, Ivan Yotov has authored 82 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Computational Mechanics, 41 papers in Computational Theory and Mathematics and 29 papers in Mechanics of Materials. Recurrent topics in Ivan Yotov's work include Advanced Numerical Methods in Computational Mathematics (71 papers), Advanced Mathematical Modeling in Engineering (40 papers) and Electromagnetic Simulation and Numerical Methods (25 papers). Ivan Yotov is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (71 papers), Advanced Mathematical Modeling in Engineering (40 papers) and Electromagnetic Simulation and Numerical Methods (25 papers). Ivan Yotov collaborates with scholars based in United States, Chile and United Kingdom. Ivan Yotov's co-authors include Mary F. Wheeler, Todd Arbogast, Mary F. Wheeler, Friedhelm Schieweck, William Layton, Béatrice Rivière, Gergina Pencheva, Konstantin Lipnikov, Guangri Xue and Benjamin Ganis and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and Mathematics of Computation.

In The Last Decade

Ivan Yotov

80 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ivan Yotov United States 30 2.9k 1.7k 1.1k 492 307 82 3.5k
Todd Arbogast United States 29 2.7k 0.9× 2.0k 1.2× 1.4k 1.2× 305 0.6× 332 1.1× 96 3.6k
Eric T. Chung Hong Kong 32 2.6k 0.9× 2.1k 1.2× 2.0k 1.8× 609 1.2× 178 0.6× 204 3.5k
Guglielmo Scovazzi United States 26 2.3k 0.8× 545 0.3× 706 0.6× 206 0.4× 219 0.7× 77 2.6k
Daniele A. Di Pietro France 27 2.9k 1.0× 905 0.5× 1.1k 1.0× 629 1.3× 635 2.1× 96 3.2k
Paola F. Antonietti Italy 24 1.6k 0.6× 700 0.4× 992 0.9× 490 1.0× 246 0.8× 107 2.0k
Victor Ginting United States 19 914 0.3× 748 0.4× 611 0.5× 122 0.2× 155 0.5× 60 1.3k
Urmila Ghia United States 16 3.4k 1.1× 183 0.1× 432 0.4× 450 0.9× 251 0.8× 99 3.9k
Abimael F. D. Loula Brazil 19 977 0.3× 370 0.2× 668 0.6× 257 0.5× 144 0.5× 92 1.4k
M. Fortin Canada 22 1.7k 0.6× 518 0.3× 758 0.7× 337 0.7× 313 1.0× 47 2.3k
Giancarlo Sangalli Italy 38 4.8k 1.6× 1.6k 0.9× 2.1k 1.9× 218 0.4× 765 2.5× 95 5.3k

Countries citing papers authored by Ivan Yotov

Since Specialization
Citations

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

Fields of papers citing papers by Ivan Yotov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ivan Yotov

This figure shows the co-authorship network connecting the top 25 collaborators of Ivan Yotov. A scholar is included among the top collaborators of Ivan Yotov 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 Ivan Yotov. Ivan Yotov 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.
2.
Wang, Xing & Ivan Yotov. (2025). A Lagrange multiplier formulation for the fully dynamic Navier–Stokes–Biot system. IMA Journal of Numerical Analysis.
3.
Yotov, Ivan, et al.. (2024). Multiscale mortar mixed finite element methods for the Biot system of poroelasticity. Computer Methods in Applied Mechanics and Engineering. 435. 117597–117597. 2 indexed citations
4.
Boon, Wietse M., Dennis Gläser, Rainer Helmig, & Ivan Yotov. (2023). Flux-mortar mixed finite element methods with multipoint flux approximation. Computer Methods in Applied Mechanics and Engineering. 405. 115870–115870. 1 indexed citations
5.
Li, Tongtong, Sergio Caucao, & Ivan Yotov. (2023). An augmented fully mixed formulation for the quasistatic Navier–Stokes–Biot model. IMA Journal of Numerical Analysis. 44(2). 1153–1210. 8 indexed citations
6.
Caucao, Sergio, et al.. (2022). A three-field Banach spaces-based mixed formulation for the unsteady Brinkman–Forchheimer equations. Computer Methods in Applied Mechanics and Engineering. 394. 114895–114895. 15 indexed citations
7.
Caucao, Sergio, et al.. (2022). A vorticity-based mixed formulation for the unsteady Brinkman–Forchheimer equations. Computer Methods in Applied Mechanics and Engineering. 404. 115829–115829. 6 indexed citations
8.
Ruíz-Baier, Ricardo, Matteo Taffetani, Hans D. Westermeyer, & Ivan Yotov. (2021). The Biot–Stokes coupling using total pressure: Formulation, analysis and application to interfacial flow in the eye. Computer Methods in Applied Mechanics and Engineering. 389. 114384–114384. 28 indexed citations
9.
Ervin, Vincent J., et al.. (2018). A nonlinear Stokes-Biot model for the interaction of a non-Newtonian fluid with poroelastic media I: well-posedness of the model. arXiv (Cornell University). 1 indexed citations
10.
11.
Ganis, Benjamin, Kundan Kumar, Gergina Pencheva, Mary F. Wheeler, & Ivan Yotov. (2014). A Global Jacobian Method for Mortar Discretizations of a Fully Implicit Two-Phase Flow Model. Multiscale Modeling and Simulation. 12(4). 1401–1423. 19 indexed citations
12.
Lipnikov, Konstantin, et al.. (2013). Discontinuous Galerkin and mimetic finite difference methods for coupled Stokes–Darcy flows on polygonal and polyhedral grids. Numerische Mathematik. 126(2). 321–360. 65 indexed citations
13.
Wheeler, Mary F., Guangri Xue, & Ivan Yotov. (2012). Local Velocity Postprocessing for Multipoint Flux Methods on General Hexahedra. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 9(3). 607–627. 3 indexed citations
14.
Clermont, Gilles, et al.. (2012). A three-dimensional mathematical and computational model of necrotizing enterocolitis. Journal of Theoretical Biology. 322. 17–32. 14 indexed citations
15.
Ganis, Benjamin, et al.. (2011). A Stochastic Mortar Mixed Finite Element Method for Flow in Porous Media with Multiple Rock Types. SIAM Journal on Scientific Computing. 33(3). 1439–1474. 13 indexed citations
16.
Wheeler, Mary F., Guangri Xue, & Ivan Yotov. (2011). A multipoint flux mixed finite element method on distorted quadrilaterals and hexahedra. Numerische Mathematik. 121(1). 165–204. 64 indexed citations
17.
Wheeler, Mary F., et al.. (2010). A Multipoint Flux Mixed Finite Element Method on Hexahedra. SIAM Journal on Numerical Analysis. 48(4). 1281–1312. 53 indexed citations
18.
Yotov, Ivan, et al.. (2009). Coupling Stokes–Darcy Flow with Transport. SIAM Journal on Scientific Computing. 31(5). 3661–3684. 66 indexed citations
19.
Peszyńska, Małgorzata, Mary F. Wheeler, & Ivan Yotov. (2002). Mortar Upscaling for Multiphase Flow in Porous Media. Computational Geosciences. 6(1). 73–100. 93 indexed citations
20.
Yotov, Ivan. (2001). Interface solvers and preconditioners of domain decomposition type for multiphase flow in multiblock porous media. Nova Science Publishers, Inc. eBooks. 157–167. 10 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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