Mario Bebendorf

3.3k total citations · 1 hit paper
44 papers, 2.1k citations indexed

About

Mario Bebendorf is a scholar working on Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Mario Bebendorf has authored 44 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Atomic and Molecular Physics, and Optics, 21 papers in Electrical and Electronic Engineering and 20 papers in Computational Theory and Mathematics. Recurrent topics in Mario Bebendorf's work include Electromagnetic Scattering and Analysis (35 papers), Electromagnetic Simulation and Numerical Methods (21 papers) and Matrix Theory and Algorithms (18 papers). Mario Bebendorf is often cited by papers focused on Electromagnetic Scattering and Analysis (35 papers), Electromagnetic Simulation and Numerical Methods (21 papers) and Matrix Theory and Algorithms (18 papers). Mario Bebendorf collaborates with scholars based in Germany, Switzerland and Kenya. Mario Bebendorf's co-authors include Sergej Rjasanow, Wolfgang Hackbusch, Stefan Kunis, Ronald Kriemann, Jasmin Smajić, Lothar Gaul, Paul E. Rapp, Dominik Brunner, M. Junge and Thorsten Steinmetz and has published in prestigious journals such as Proceedings of the IEEE, Mathematics of Computation and SIAM Journal on Numerical Analysis.

In The Last Decade

Mario Bebendorf

40 papers receiving 2.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Approximation of boundary element matrices

2000 575 citations Mario Bebendorf Numerische Mathematik profile →

Peers

Mario Bebendorf

AMPO

EEE

MM

CTM

CM

Sergej Rjasanow Germany

Stefan Sauter Switzerland

Steffen Börm Germany

Luc Giraud France

Olaf Steinbach Austria

Jiguang Sun United States

Ralf Hiptmair Switzerland

Helmut Harbrecht Switzerland

Zydrunas Gimbutas United States

Karl Meerbergen Belgium

Sergej Rjasanow Germany

Mario Bebendorf

721 ×0.5

AMPO

640 ×0.6

EEE

524 ×0.7

MM

184 ×0.3

CTM

497 ×0.9

CM

Citations per year, relative to Mario Bebendorf Mario Bebendorf (= 1×) peers Sergej Rjasanow

Countries citing papers authored by Mario Bebendorf

Since Specialization

Citations

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

Fields of papers citing papers by Mario Bebendorf

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mario Bebendorf

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

All Works

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20 of 20 papers shown

1.

Bauer, Maximilian, et al.. (2022). Kernel-independent adaptive construction of H²-matrix approximations. EPub Bayreuth (University of Bayreuth). 1 indexed citations

2.

Gathungu, Duncan Kioi, Mario Bebendorf, & Alfio Borzı̀. (2021). Hierarchical‐matrix method for a class of diffusion‐dominated partial integro‐differential equations. Numerical Linear Algebra with Applications. 29(1). 1 indexed citations

3.

Bebendorf, Mario, et al.. (2021). Kernel-independent adaptive construction of $$\mathcal {H}^2$$-matrix approximations. Numerische Mathematik. 150(1). 1–32. 6 indexed citations

4.

Bebendorf, Mario, et al.. (2016). On the spectral equivalence of hierarchical matrix preconditioners for elliptic problems. Mathematics of Computation. 85(302). 2839–2861. 4 indexed citations

5.

Bebendorf, Mario, et al.. (2014). Separation of Variables for Function Generated High-Order Tensors. Journal of Scientific Computing. 61(1). 145–165. 4 indexed citations

6.

Bebendorf, Mario, et al.. (2012). Hierarchical Matrix Preconditioning for Low-Frequency–Full-Maxwell Simulations. Proceedings of the IEEE. 101(2). 423–433. 8 indexed citations

7.

Brunner, Dominik, M. Junge, Paul E. Rapp, Mario Bebendorf, & Lothar Gaul. (2010). Comparison of the Fast Multipole Method with Hierarchical Matrices for the Helmholtz-BEM. Computer Modeling in Engineering & Sciences. 58(2). 131–160. 33 indexed citations

8.

Bebendorf, Mario & Thomas Fischer. (2010). On the purely algebraic data-sparse approximation of the inverse and the triangular factors of sparse matrices. Numerical Linear Algebra with Applications. 18(1). 105–122. 6 indexed citations

9.

Bebendorf, Mario, et al.. (2010). ${\cal H}$-Matrix-Based Operator Preconditioning for Full Maxwell at Low Frequencies. IEEE Transactions on Magnetics. 46(8). 3193–3196. 7 indexed citations

10.

Bebendorf, Mario & Stefan Kunis. (2009). Recompression techniques for adaptive cross approximation. Journal of Integral Equations and Applications. 21(3). 57 indexed citations

11.

Bebendorf, Mario. (2009). Parallel Hierarchical Matrix Preconditioners for the Curl-Curl Operator. Journal of Computational Mathematics. 27(5). 624–641. 8 indexed citations

12.

Bebendorf, Mario. (2008). Hierarchical Matrices : A Means to Efficiently Solve Elliptic Boundary Value Problems. CERN Document Server (European Organization for Nuclear Research). 153 indexed citations

13.

Bebendorf, Mario. (2008). Hierarchical Matrices. 29 indexed citations

14.

Steinmetz, Thorsten, et al.. (2008). Efficient Symmetric FEM-BEM Coupled Simulations of Electro-Quasistatic Fields. IEEE Transactions on Magnetics. 44(6). 1346–1349. 16 indexed citations

15.

Bebendorf, Mario. (2007). Why Finite Element Discretizations Can Be Factored by Triangular Hierarchical Matrices. SIAM Journal on Numerical Analysis. 45(4). 1472–1494. 49 indexed citations

16.

Smajić, Jasmin, et al.. (2007). Fast BEM for Eddy-Current Problems Using H-Matrices and Adaptive Cross Approximation. IEEE Transactions on Magnetics. 43(4). 1269–1272. 19 indexed citations

17.

Bebendorf, Mario, et al.. (2006). Accelerating Galerkin BEM for linear elasticity using adaptive cross approximation. Mathematical Methods in the Applied Sciences. 29(14). 1721–1747. 77 indexed citations

18.

Bebendorf, Mario. (2005). On the numerical solution of convection-dominated problems using hierarchical matrices.

19.

Bebendorf, Mario & Sergej Rjasanow. (2003). Adaptive Low-Rank Approximation of Collocation Matrices. Computing. 70(1). 1–24. 365 indexed citations

20.

Bebendorf, Mario. (1999). Low-rank approximation of boundary element matrices. Publications of the UdS (Saarland University). 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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