Isabel Beichl
Impact in
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods
-
- Computational Geometry and Mesh Generation
Papers in
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- Markov Chains and Monte Carlo Methods 11
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- Computational Geometry and Mesh Generation 4
- Co-authors
- Frank SullivanJames L. BlueEnrico PuppoDianne P. O’LearyAttilio L. StellaMaria Carla TesiEnzo OrlandiniT. L. Einstein
- Journals
- Computing in Science & Engineering (13 papers)Algorithmica (1 paper)Physical Review Letters (1 paper)Journal of Computational Physics (1 paper)Computational Statistics (1 paper)
- Partner nations
- United StatesEgyptRussia
In The Last Decade
Isabel Beichl
31 papers receiving 357 citations
Peers
Comparison fields: 5 of 93
- Statistics and Probability 68
- Computer Graphics and Computer-Aided Design 27
- Condensed Matter Physics 77
- Mathematical Physics 45
- Statistics, Probability and Uncertainty 31
Countries citing papers authored by Isabel Beichl
This map shows the geographic impact of Isabel Beichl'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 Isabel Beichl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Isabel Beichl more than expected).
Fields of papers citing papers by Isabel Beichl
This network shows the impact of papers produced by Isabel Beichl. 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 Isabel Beichl. The network helps show where Isabel Beichl may publish in the future.
Co-authorship network
The 11 scholars most cited alongside Isabel Beichl, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Stratified Sampling for the Ising Model: A Graph-Theoretic Approach | NIST | 2013 | 0 |
| 2 | 2011 | 0 | |
| 3 | 2011 | 1 | |
| 4 | 2011 | 1 | |
| 5 | Computing Network Reliability Coefficients | NIST | 2011 | 0 |
| 6 | An Approximation Algorithm for the Coefficients of the Reliability Polynomial | NIST | 2010 | 2 |
| 7 | 2008 | 4 | |
| 8 | Generating Network Models Using the S-Metric | 2008 | 2 |
| 9 | 2008 | 1 | |
| 10 | 2007 | 0 | |
| 11 | 2006 | 1 | |
| 12 | 2001 | 11 | |
| 13 | 2000 | 113 | |
| 14 | 1999 | 22 | |
| 15 | 1999 | 28 | |
| 16 | 1998 | 1 | |
| 17 | 1996 | 1 | |
| 18 | 1996 | 6 | |
| 19 | 1996 | 1 | |
| 20 | 1992 | 18 |
About Isabel Beichl
Isabel Beichl is a scholar working on Statistics and Probability, Computer Graphics and Computer-Aided Design, Mathematical Physics, Condensed Matter Physics and Statistical and Nonlinear Physics, having authored 37 papers that have together received 388 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (11 papers), Stochastic processes and statistical mechanics (8 papers), Theoretical and Computational Physics (8 papers), Complex Network Analysis Techniques (5 papers), Computational Geometry and Mesh Generation (4 papers), Opinion Dynamics and Social Influence (4 papers), Advanced Chemical Physics Studies (4 papers) and Advanced Database Systems and Queries (3 papers). The work is most often cited by research in Statistics and Probability (68 citations), Computer Graphics and Computer-Aided Design (27 citations), Condensed Matter Physics (77 citations), Mathematical Physics (45 citations) and Statistics, Probability and Uncertainty (31 citations). Isabel Beichl has collaborated with scholars based in United States, Egypt and Russia. Frequent co-authors include Frank Sullivan, James L. Blue, Enrico Puppo, Dianne P. O’Leary, Attilio L. Stella, Maria Carla Tesi, Enzo Orlandini, T. L. Einstein, Francis Sullivan and David G. Harris. Their work appears in journals such as Computing in Science & Engineering, Algorithmica, Physical Review Letters, Journal of Computational Physics and Computational Statistics.
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.