Brian M. de Silva
- Statistical and Nonlinear Physics top 5%
- Control and Systems Engineering top 10%
- Artificial Intelligence
- Statistics, Probability and Uncertainty top 5%
- Computational Mechanics
- Co-authors
- Steven L. BruntonJ. Nathan KutzKathleen ChampionJean-Christophe LoiseauMarkus QuadeJared CallahamCharles B. DelahuntKadierdan Kaheman
- Topics
- Model Reduction and Neural Networks (3 papers)Probabilistic and Robust Engineering Design (2 papers)Anomaly Detection Techniques and Applications (2 papers)
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyControl and Systems Engineering
- Journals
- AIAA JournalThe Journal of Open Source SoftwareApplied Mathematics
- Partner nations
- United StatesFrance
In The Last Decade
Brian M. de Silva
6 papers receiving 256 citations
Hit Papers
Peers
Comparison fields: 5 of 62
- Statistical and Nonlinear Physics 157
- Control and Systems Engineering 78
- Artificial Intelligence 64
- Statistics, Probability and Uncertainty 54
- Computational Mechanics 42
Countries citing papers authored by Brian M. de Silva
This map shows the geographic impact of Brian M. de Silva'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 Brian M. de Silva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian M. de Silva more than expected).
Fields of papers citing papers by Brian M. de Silva
This network shows the impact of papers produced by Brian M. de Silva. 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 Brian M. de Silva. The network helps show where Brian M. de Silva may publish in the future.
Co-authorship network of co-authors of Brian M. de Silva
This figure shows the co-authorship network connecting the top 25 collaborators of Brian M. de Silva. A scholar is included among the top collaborators of Brian M. de Silva 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 Brian M. de Silva. Brian M. de Silva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | PySINDy: A comprehensive Python package for robust sparse system identificationbreakdown → | 105 |
| 3 | 11 | |
| 4 | 13 | |
| 5 | 126 | |
| 6 | 2 |
About Brian M. de Silva
Brian M. de Silva is a scholar working on Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics and Biophysics, having authored 6 papers that have together received 264 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (3 papers), Probabilistic and Robust Engineering Design (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (157 citations), Statistics, Probability and Uncertainty (54 citations) and Control and Systems Engineering (78 citations). Brian M. de Silva has collaborated with scholars based in United States and France. Frequent co-authors include Steven L. Brunton, J. Nathan Kutz, Kathleen Champion, Jean-Christophe Loiseau, Markus Quade, Jared Callaham, Charles B. Delahunt, Kadierdan Kaheman, Urban Fasel and Alan A. Kaptanoglu. Their work appears in journals such as AIAA Journal, The Journal of Open Source Software and Applied Mathematics.
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.