Brian M. de Silva

551 total citations · 1 hit paper
6 papers, 264 citations indexed

About

Brian M. de Silva is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Control and Systems Engineering. According to data from OpenAlex, Brian M. de Silva has authored 6 papers receiving a total of 264 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Statistical and Nonlinear Physics and 2 papers in Control and Systems Engineering. Recurrent topics in Brian M. de Silva's work include Model Reduction and Neural Networks (3 papers), Probabilistic and Robust Engineering Design (2 papers) and Anomaly Detection Techniques and Applications (2 papers). Brian M. de Silva is often cited by papers focused on Model Reduction and Neural Networks (3 papers), Probabilistic and Robust Engineering Design (2 papers) and Anomaly Detection Techniques and Applications (2 papers). Brian M. de Silva collaborates with scholars based in United States and France. Brian M. de Silva's co-authors include Steven L. Brunton, J. Nathan Kutz, Kathleen Champion, Jean-Christophe Loiseau, Markus Quade, Jared Callaham, Urban Fasel, Charles B. Delahunt, Zachary G. Nicolaou and Alan A. Kaptanoglu and has published in prestigious journals such as AIAA Journal, The Journal of Open Source Software and Applied Mathematics.

In The Last Decade

Brian M. de Silva

6 papers receiving 256 citations

Hit Papers

align trajectories sort by overperformance

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.

PySINDy: A comprehensive Python package for robust sparse system identification

2022 105 citations Alan A. Kaptanoglu, Brian M. de Silva et al. The Journal of Open Source Software profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian M. de Silva United States	5	157	78	64	54	42	6	264
Kadierdan Kaheman United States	4	205 1.3×	104 1.3×	88 1.4×	71 1.3×	44 1.0×	6	332
Kathleen Champion United States	3	221 1.4×	95 1.2×	82 1.3×	75 1.4×	52 1.2×	4	339
Markus Quade Germany	6	127 0.8×	58 0.7×	85 1.3×	56 1.0×	27 0.6×	8	248
Yeonjong Shin United States	11	177 1.1×	49 0.6×	99 1.5×	85 1.6×	103 2.5×	20	342
Travis Askham United States	6	151 1.0×	61 0.8×	48 0.8×	53 1.0×	87 2.1×	13	297
Yiping Lu China	5	260 1.7×	31 0.4×	101 1.6×	67 1.2×	93 2.2×	17	386
Pengzhan Jin China	7	200 1.3×	34 0.4×	102 1.6×	24 0.4×	51 1.2×	10	291
Markus Abel Australia	7	77 0.5×	41 0.5×	61 1.0×	50 0.9×	27 0.6×	14	245
Liqian Peng United States	9	154 1.0×	41 0.5×	30 0.5×	40 0.7×	103 2.5×	20	318
Tong Qin United States	7	203 1.3×	39 0.5×	68 1.1×	82 1.5×	165 3.9×	11	387

Countries citing papers authored by Brian M. de Silva

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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6 of 6 papers shown

1.

Pan, Shaowu, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, & Steven L. Brunton. (2024). PyKoopman: A Python Package for Data-DrivenApproximation of the Koopman Operator. The Journal of Open Source Software. 9(94). 5881–5881. 7 indexed citations

2.

Kaptanoglu, Alan A., Brian M. de Silva, Urban Fasel, et al.. (2022). PySINDy: A comprehensive Python package for robust sparse system identification. The Journal of Open Source Software. 7(69). 3994–3994. 105 indexed citations breakdown →

3.

Silva, Brian M. de, et al.. (2021). PySensors: A Python package for sparse sensor placement. The Journal of Open Source Software. 6(58). 2828–2828. 11 indexed citations

4.

Silva, Brian M. de, et al.. (2021). Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data. AIAA Journal. 59(9). 3490–3503. 13 indexed citations

5.

Silva, Brian M. de, Kathleen Champion, Markus Quade, et al.. (2020). PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data. The Journal of Open Source Software. 5(49). 2104–2104. 126 indexed citations

6.

Ascenzi, Maria‐Grazia, Xia Du, Brian M. de Silva, et al.. (2014). Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone. Applied Mathematics. 5(18). 2866–2880. 2 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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