Maya Neytcheva

1.2k total citations
61 papers, 807 citations indexed

About

Maya Neytcheva is a scholar working on Computational Theory and Mathematics, Computational Mechanics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Maya Neytcheva has authored 61 papers receiving a total of 807 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Computational Theory and Mathematics, 42 papers in Computational Mechanics and 21 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Maya Neytcheva's work include Advanced Numerical Methods in Computational Mathematics (42 papers), Matrix Theory and Algorithms (42 papers) and Electromagnetic Scattering and Analysis (21 papers). Maya Neytcheva is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (42 papers), Matrix Theory and Algorithms (42 papers) and Electromagnetic Scattering and Analysis (21 papers). Maya Neytcheva collaborates with scholars based in Sweden, Czechia and Netherlands. Maya Neytcheva's co-authors include Owe Axelsson, Bashir Ahmad, Radim Blaheta, Stefano Serra‐Capizzano, Xin He, Minh Do‐Quang, Zhao‐Zheng Liang, Svetozar Margenov, Martin Kronbichler and Ben Polman and has published in prestigious journals such as Computer Methods in Applied Mechanics and Engineering, SIAM Journal on Scientific Computing and Computers & Mathematics with Applications.

In The Last Decade

Maya Neytcheva

56 papers receiving 729 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maya Neytcheva Sweden 16 621 551 345 226 114 61 807
Walter Zulehner Austria 19 564 0.9× 711 1.3× 218 0.6× 259 1.1× 170 1.5× 43 1.0k
Qiya Hu China 14 225 0.4× 389 0.7× 149 0.4× 169 0.7× 171 1.5× 58 616
Ivar Gustafsson Sweden 12 523 0.8× 503 0.9× 206 0.6× 223 1.0× 169 1.5× 25 777
Yongzhong Song China 18 423 0.7× 168 0.3× 145 0.4× 621 2.7× 46 0.4× 85 907
Jennifer Pestana United Kingdom 13 271 0.4× 229 0.4× 143 0.4× 119 0.5× 41 0.4× 35 507
Artem Napov Belgium 9 260 0.4× 255 0.5× 144 0.4× 71 0.3× 37 0.3× 24 467
Hehu Xie China 20 273 0.4× 677 1.2× 105 0.3× 308 1.4× 439 3.9× 71 955
Ken Hayami Japan 14 185 0.3× 147 0.3× 181 0.5× 149 0.7× 156 1.4× 39 547
Wei-Pai Tang United States 13 341 0.5× 281 0.5× 200 0.6× 104 0.5× 35 0.3× 22 500
Tarek P. Mathew United States 11 379 0.6× 618 1.1× 110 0.3× 238 1.1× 236 2.1× 18 837

Countries citing papers authored by Maya Neytcheva

Since Specialization
Citations

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

Fields of papers citing papers by Maya Neytcheva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maya Neytcheva

This figure shows the co-authorship network connecting the top 25 collaborators of Maya Neytcheva. A scholar is included among the top collaborators of Maya Neytcheva 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 Maya Neytcheva. Maya Neytcheva 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.
Serra‐Capizzano, Stefano, et al.. (2024). Spectral Analysis of Preconditioned Matrices Arising from Stage-Parallel Implicit Runge–Kutta Methods of Arbitrarily High Order. SIAM Journal on Matrix Analysis and Applications. 45(2). 1007–1034. 3 indexed citations
2.
Münch, Peter, et al.. (2023). Stage-Parallel Fully Implicit Runge–Kutta Implementations with Optimal Multilevel Preconditioners at the Scaling Limit. SIAM Journal on Scientific Computing. 46(2). S71–S96. 7 indexed citations
3.
4.
Weiß, Michael, Maya Neytcheva, & Thomas Kalscheuer. (2022). Iterative solution methods for 3D controlled-source electromagnetic forward modelling of geophysical exploration scenarios. Computational Geosciences. 27(1). 81–102. 5 indexed citations
5.
Cao, Yang & Maya Neytcheva. (2021). Cell‐by‐cell approximate Schur complement technique in preconditioning of meshfree discretized piezoelectric equations. Numerical Linear Algebra with Applications. 28(4). 2 indexed citations
6.
Donatelli, Marco, et al.. (2017). Function-based block multigrid strategy for a two-dimensional linear elasticity-type problem. Computers & Mathematics with Applications. 74(5). 1015–1028. 4 indexed citations
7.
Axelsson, Owe, et al.. (2016). A preconditioner for optimal control problems, constrained by Stokes equation with a time-harmonic control. Journal of Computational and Applied Mathematics. 310. 5–18. 26 indexed citations
8.
Axelsson, Owe, et al.. (2015). Preconditioning of iterative methods ‐ theory and applications. Numerical Linear Algebra with Applications. 22(6). 901–902. 1 indexed citations
9.
Neytcheva, Maya, et al.. (2013). Efficient numerical solution of discrete multi-component Cahn–Hilliard systems. Computers & Mathematics with Applications. 67(1). 106–121. 27 indexed citations
10.
Axelsson, Owe, Maya Neytcheva, & Bashir Ahmad. (2013). A comparison of iterative methods to solve complex valued linear algebraic systems. Numerical Algorithms. 66(4). 811–841. 97 indexed citations
11.
Axelsson, Owe, et al.. (2012). Numerical and computational efficiency of solvers for two-phase problems. Computers & Mathematics with Applications. 65(3). 301–314. 25 indexed citations
12.
He, Xin, et al.. (2012). Efficiently parallel implementation of the inverse Sherman–Morrison algorithm. KTH Publication Database DiVA (KTH Royal Institute of Technology).
13.
Do‐Quang, Minh, et al.. (2012). Efficient Preconditioners for Large Scale Binary Cahn-Hilliard Models. Computational Methods in Applied Mathematics. 12(1). 1–22. 25 indexed citations
14.
Neytcheva, Maya. (2010). On element-by-element Schur complement approximations. Linear Algebra and its Applications. 434(11). 2308–2324. 19 indexed citations
15.
Margenov, Svetozar, et al.. (2010). Robust AMLI methods for parabolic Crouzeix–Raviart FEM systems. Journal of Computational and Applied Mathematics. 235(2). 380–390. 2 indexed citations
16.
Neytcheva, Maya, et al.. (2008). Preconditioning of nonsymmetric saddle point systems as arising in modelling of viscoelastic problems.. ETNA - Electronic Transactions on Numerical Analysis. 29. 193–211. 11 indexed citations
17.
Blaheta, Radim, et al.. (2007). Schwarz methods for discrete elliptic and parabolic problems with an application to nuclear waste repository modelling. Mathematics and Computers in Simulation. 76(1-3). 18–27. 5 indexed citations
18.
Axelsson, Owe, Vincent A. Barker, Maya Neytcheva, & Ben Polman. (2001). Solving the Stokes Problem on a Massively Parallel Computer. Mathematical Modelling and Analysis. 6(1). 7–27. 15 indexed citations
19.
Neytcheva, Maya, et al.. (1996). Some basic facts for efficient massively parallel computation. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. 9. 9–17. 3 indexed citations
20.
Axelsson, Owe & Maya Neytcheva. (1995). Scalable algorithms for the solution of Navier's equations of elasticity. Journal of Computational and Applied Mathematics. 63(1-3). 149–178. 13 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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