Robert Scheichl

4.0k total citations
85 papers, 2.1k citations indexed

About

Robert Scheichl is a scholar working on Computational Mechanics, Computational Theory and Mathematics and Mechanics of Materials. According to data from OpenAlex, Robert Scheichl has authored 85 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computational Mechanics, 39 papers in Computational Theory and Mathematics and 23 papers in Mechanics of Materials. Recurrent topics in Robert Scheichl's work include Advanced Numerical Methods in Computational Mathematics (41 papers), Advanced Mathematical Modeling in Engineering (30 papers) and Probabilistic and Robust Engineering Design (23 papers). Robert Scheichl is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (41 papers), Advanced Mathematical Modeling in Engineering (30 papers) and Probabilistic and Robust Engineering Design (23 papers). Robert Scheichl collaborates with scholars based in United Kingdom, Germany and United States. Robert Scheichl's co-authors include Aretha L. Teckentrup, Ivan G. Graham, Michael B. Giles, K. A. Cliffe, Clemens Pechstein, Tim Dodwell, Frances Y. Kuo, Ian H. Sloan, Eike H. Müller and Victorita Dolean and has published in prestigious journals such as The Journal of Chemical Physics, Journal of Computational Physics and Journal of Membrane Science.

In The Last Decade

Robert Scheichl

78 papers receiving 1.9k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Robert Scheichl 1.0k 867 705 492 360 85 2.1k
Ch. Schwab 586 0.6× 282 0.3× 341 0.5× 300 0.6× 209 0.6× 28 1.1k
Oliver G. Ernst 357 0.4× 519 0.6× 448 0.6× 121 0.2× 254 0.7× 39 1.3k
K. A. Cliffe 1.4k 1.4× 241 0.3× 253 0.4× 187 0.4× 213 0.6× 94 2.2k
Serge Prudhomme 1.3k 1.3× 520 0.6× 355 0.5× 925 1.9× 108 0.3× 87 2.5k
Tan Bui–Thanh 806 0.8× 255 0.3× 692 1.0× 190 0.4× 194 0.5× 58 2.0k
Ivan G. Graham 1.1k 1.1× 851 1.0× 217 0.3× 1.1k 2.2× 574 1.6× 80 2.7k
Georgios E. Zouraris 433 0.4× 421 0.5× 757 1.1× 135 0.3× 196 0.5× 27 1.2k
Clayton Webster 500 0.5× 539 0.6× 1.3k 1.9× 117 0.2× 142 0.4× 45 1.8k
Gregory E. Fasshauer 1.0k 1.0× 216 0.2× 113 0.2× 1.7k 3.4× 382 1.1× 60 2.7k
Robert Eymard 2.2k 2.1× 1.0k 1.2× 68 0.1× 433 0.9× 579 1.6× 152 3.5k

Countries citing papers authored by Robert Scheichl

Since Specialization
Citations

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

Fields of papers citing papers by Robert Scheichl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robert Scheichl

This figure shows the co-authorship network connecting the top 25 collaborators of Robert Scheichl. A scholar is included among the top collaborators of Robert Scheichl 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 Robert Scheichl. Robert Scheichl 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.
Scheichl, Robert, et al.. (2025). A mixed multiscale spectral generalized finite element method. Numerische Mathematik. 157(1). 1–40. 4 indexed citations
2.
3.
Scheichl, Robert, et al.. (2025). Statistical parameter identification of mixed-mode patterns from a single experimental snapshot. Journal of Computational Physics. 543. 114384–114384.
4.
Sanderse, Benjamin, et al.. (2024). Scientific Machine Learning: A Symbiosis. 7(1). i–x.
5.
Herzog, Roland, et al.. (2024). Tractable optimal experimental design using transport maps*. Inverse Problems. 40(12). 125002–125002.
6.
Cui, Tiangang, et al.. (2024). Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems. Journal of Scientific Computing. 99(3). 77–77.
7.
Klein, Ole, et al.. (2023). parafields: A generator for distributed, stationaryGaussian processes. The Journal of Open Source Software. 8(92). 5735–5735.
8.
Gilbert, Alexander D. & Robert Scheichl. (2023). Multilevel quasi-Monte Carlo for random elliptic eigenvalue problems II: efficient algorithms and numerical results. IMA Journal of Numerical Analysis. 44(1). 504–535. 4 indexed citations
9.
Dodwell, Tim, et al.. (2023). Multilevel Delayed Acceptance MCMC. SIAM/ASA Journal on Uncertainty Quantification. 11(1). 1–30. 5 indexed citations
10.
Scheichl, Robert, et al.. (2022). Error estimates for discrete generalized FEMs with locally optimal spectral approximations. Mathematics of Computation. 2 indexed citations
11.
Dodwell, Tim, C Buchanan, Pinelopi Kyvelou, et al.. (2021). A data-centric approach to generative modelling for 3D-printed steel. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 477(2255). 20210444–20210444. 13 indexed citations
12.
Kruse, Jakob, et al.. (2019). HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference.. arXiv (Cornell University). 2 indexed citations
13.
Graham, Ivan G., Frances Y. Kuo, Dirk Nuyens, Robert Scheichl, & Ian H. Sloan. (2018). Circulant embedding with QMC: analysis for elliptic PDE with lognormal coefficients. Numerische Mathematik. 140(2). 479–511. 15 indexed citations
14.
Müller, Eike H., Robert Scheichl, & Tony Shardlow. (2015). Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 471(2176). 20140679–20140679. 7 indexed citations
15.
Dodwell, Tim, et al.. (2015). A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow. SIAM/ASA Journal on Uncertainty Quantification. 3(1). 1075–1108. 76 indexed citations
16.
Scheichl, Robert, et al.. (2013). Large Scale Inverse Problems. Directory of Open access Books (OAPEN Foundation). 9 indexed citations
17.
Kyprianou, Andreas E., et al.. (2013). Multilevel Monte Carlo simulation for Lévy processes based on the Wiener–Hopf factorisation. Stochastic Processes and their Applications. 124(2). 985–1010. 20 indexed citations
18.
Marletta, Marco & Robert Scheichl. (2012). Eigenvalues in spectral gaps of differential operators. Journal of Spectral Theory. 2(3). 293–320. 15 indexed citations
19.
Spillane, Nicole, Victorita Dolean, Patrice Hauret, et al.. (2011). A robust two-level domain decomposition preconditioner for systems of PDEs. Comptes Rendus Mathématique. 349(23-24). 1255–1259. 29 indexed citations
20.
Scheichl, Robert & Eero Vainikko. (2006). Robust aggregation-based coarsening for additive Schwarz in the case of highly variable coefficients. Research Repository (Delft University of Technology). 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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