Michael G. Kapteyn

489 total citations
8 papers, 270 citations indexed

About

Michael G. Kapteyn is a scholar working on Control and Systems Engineering, Statistics, Probability and Uncertainty and Statistical and Nonlinear Physics. According to data from OpenAlex, Michael G. Kapteyn has authored 8 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Control and Systems Engineering, 3 papers in Statistics, Probability and Uncertainty and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Michael G. Kapteyn's work include Probabilistic and Robust Engineering Design (3 papers), Digital Transformation in Industry (2 papers) and Model Reduction and Neural Networks (2 papers). Michael G. Kapteyn is often cited by papers focused on Probabilistic and Robust Engineering Design (3 papers), Digital Transformation in Industry (2 papers) and Model Reduction and Neural Networks (2 papers). Michael G. Kapteyn collaborates with scholars based in United States, New Zealand and Switzerland. Michael G. Kapteyn's co-authors include Karen Willcox, David J. Knezevic, D.B.P. Huynh, M. D. Tran, Anirban Chaudhuri, Ernesto A. B. F. Lima, David A. Hormuth, Guillermo Lorenzo, Chengyue Wu and Thomas E. Yankeelov and has published in prestigious journals such as International Journal for Numerical Methods in Engineering, Journal of Mechanical Design and Frontiers in Artificial Intelligence.

In The Last Decade

Michael G. Kapteyn

7 papers receiving 262 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael G. Kapteyn United States	5	83	48	45	44	40	8	270
Linyu Lin United States	9	46 0.6×	17 0.4×	31 0.7×	22 0.5×	108 2.7×	33	272
Arinan Dourado United States	9	18 0.2×	69 1.4×	108 2.4×	71 1.6×	106 2.6×	20	307
Ching Hsieh United States	7	127 1.5×	113 2.4×	109 2.4×	9 0.2×	26 0.7×	21	383
Roman B. Statnikov United States	11	70 0.8×	61 1.3×	90 2.0×	8 0.2×	91 2.3×	33	435
Dipankar Ghosh United States	5	46 0.6×	72 1.5×	68 1.5×	16 0.4×	21 0.5×	10	383
Kristian Amadori Sweden	13	140 1.7×	11 0.2×	82 1.8×	9 0.2×	121 3.0×	45	430
Nhu Van Nguyen South Korea	10	22 0.3×	13 0.3×	31 0.7×	19 0.4×	31 0.8×	33	381
Thiagarajan Krishnamurthy United States	8	57 0.7×	94 2.0×	68 1.5×	5 0.1×	20 0.5×	20	314
Parviz Mohammad Zadeh Iran	11	47 0.6×	51 1.1×	97 2.2×	4 0.1×	17 0.4×	18	383

Countries citing papers authored by Michael G. Kapteyn

Since Specialization

Citations

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

Fields of papers citing papers by Michael G. Kapteyn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael G. Kapteyn. 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 Michael G. Kapteyn. The network helps show where Michael G. Kapteyn may publish in the future.

Co-authorship network of co-authors of Michael G. Kapteyn

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

All Works

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8 of 8 papers shown

Kapteyn, Michael G., et al.. (2025). Digital-Twin-Enabled Multi-Spacecraft On-Orbit Operations. 1 indexed citations

Kapteyn, Michael G., et al.. (2024). Digital Twin: Graph Formulations for Managing Complexity and Uncertainty. 2141–2148.

Chaudhuri, Anirban, David A. Hormuth, Guillermo Lorenzo, et al.. (2023). Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. Frontiers in Artificial Intelligence. 6. 1222612–1222612. 41 indexed citations

Kapteyn, Michael G. & Karen Willcox. (2022). Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning. Journal of Mechanical Design. 144(9). 9 indexed citations

Kapteyn, Michael G., David J. Knezevic, D.B.P. Huynh, M. D. Tran, & Karen Willcox. (2020). Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models. International Journal for Numerical Methods in Engineering. 123(13). 2986–3003. 142 indexed citations

Kapteyn, Michael G., David J. Knezevic, & Karen Willcox. (2020). Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. AIAA Scitech 2020 Forum. 72 indexed citations

Kapteyn, Michael G., Karen Willcox, & Andy Philpott. (2019). Distributionally robust optimization for engineering design under uncertainty. International Journal for Numerical Methods in Engineering. 120(7). 835–859. 4 indexed citations

Kapteyn, Michael G., Karen Willcox, & Andy Philpott. (2018). A Distributionally Robust Approach to Black-Box Optimization. DSpace@MIT (Massachusetts Institute of Technology). 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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