John T. Betts

5.8k total citations · 2 hit papers
54 papers, 4.1k citations indexed

About

John T. Betts is a scholar working on Aerospace Engineering, Numerical Analysis and Computational Mechanics. According to data from OpenAlex, John T. Betts has authored 54 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Aerospace Engineering, 25 papers in Numerical Analysis and 15 papers in Computational Mechanics. Recurrent topics in John T. Betts's work include Spacecraft Dynamics and Control (23 papers), Aerospace Engineering and Control Systems (14 papers) and Advanced Optimization Algorithms Research (13 papers). John T. Betts is often cited by papers focused on Spacecraft Dynamics and Control (23 papers), Aerospace Engineering and Control Systems (14 papers) and Advanced Optimization Algorithms Research (13 papers). John T. Betts collaborates with scholars based in United States, Australia and Germany. John T. Betts's co-authors include William P. Huffman, Stephen L. Campbell, Paul D. Frank, Evin Cramer, Stephen Citron, Vasyl V. Tkach, Michael L. Kent, Stephen E. Greiman, Volker Schulz and Ch. Tsitouras and has published in prestigious journals such as AIAA Journal, SIAM Journal on Scientific Computing and Journal of Guidance Control and Dynamics.

In The Last Decade

John T. Betts

54 papers receiving 3.8k citations

Hit Papers

Survey of Numerical Methods for Trajectory Optimization 1998 2026 2007 2016 1998 2010 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John T. Betts United States 18 2.5k 1.1k 776 630 520 54 4.1k
Anil V. Rao United States 33 3.7k 1.5× 1.3k 1.1× 807 1.0× 699 1.1× 527 1.0× 130 6.0k
I. Michael Ross United States 34 3.4k 1.3× 1.2k 1.0× 672 0.9× 868 1.4× 492 0.9× 140 4.3k
Arthur E. Bryson United States 26 1.5k 0.6× 1.4k 1.2× 331 0.4× 159 0.3× 191 0.4× 71 3.2k
Ping Lu United States 39 4.8k 1.9× 1.2k 1.1× 918 1.2× 828 1.3× 174 0.3× 182 5.6k
Taeyoung Lee United States 27 2.0k 0.8× 2.7k 2.4× 976 1.3× 112 0.2× 202 0.4× 142 4.1k
N. Harris McClamroch United States 38 2.1k 0.9× 6.8k 6.0× 2.4k 3.1× 272 0.4× 361 0.7× 222 8.4k
Mohsen Razzaghi United States 39 627 0.2× 1.0k 0.9× 430 0.6× 107 0.2× 3.1k 6.0× 255 5.7k
Donald E. Kirk United States 6 608 0.2× 1.4k 1.2× 249 0.3× 115 0.2× 185 0.4× 20 3.0k
Youdan Kim South Korea 36 3.2k 1.3× 3.6k 3.1× 909 1.2× 104 0.2× 66 0.1× 338 6.3k
Anthony Calise United States 47 4.2k 1.7× 6.1k 5.3× 590 0.8× 134 0.2× 227 0.4× 383 8.6k

Countries citing papers authored by John T. Betts

Since Specialization
Citations

This map shows the geographic impact of John T. Betts'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 John T. Betts with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John T. Betts more than expected).

Fields of papers citing papers by John T. Betts

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John T. Betts. 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 John T. Betts. The network helps show where John T. Betts may publish in the future.

Co-authorship network of co-authors of John T. Betts

This figure shows the co-authorship network connecting the top 25 collaborators of John T. Betts. A scholar is included among the top collaborators of John T. Betts 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 John T. Betts. John T. Betts 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.
Betts, John T., et al.. (2020). Examination of solving optimal control problems with delays using GPOPS-Ⅱ. Numerical Algebra Control and Optimization. 11(2). 283–283. 4 indexed citations
2.
Betts, John T., et al.. (2019). Initial guess sensitivity in computational optimal control problems. Numerical Algebra Control and Optimization. 10(1). 39–41. 7 indexed citations
3.
Campbell, Stephen L. & John T. Betts. (2016). Comments on direct transcription solution of DAE constrained optimal control problems with two discretization approaches. Numerical Algorithms. 73(3). 807–838. 4 indexed citations
4.
Greiman, Stephen E., et al.. (2016). Nanophyetus salmincola, vector of the salmon poisoning disease agent Neorickettsia helminthoeca, harbors a second pathogenic Neorickettsia species. Veterinary Parasitology. 229. 107–109. 7 indexed citations
5.
Betts, John T., et al.. (2008). Re-ADA. 39–56. 2 indexed citations
6.
Betts, John T.. (2007). Optimal lunar swingby trajectories. The Journal of the Astronautical Sciences. 55(3). 349–371. 5 indexed citations
7.
Betts, John T.. (2006). Trajectory optimization in the presence of uncertainty. The Journal of the Astronautical Sciences. 54(2). 227–243. 1 indexed citations
8.
Schulz, Volker, et al.. (2006). Optimal flight trajectories for the validation of aerodynamic models. Optimization methods & software. 21(6). 889–900. 4 indexed citations
9.
Betts, John T., et al.. (2004). Initialization of direct transcription optimal control software. mea lr 85. 3802–3807. 9 indexed citations
10.
Betts, John T., et al.. (2003). Computing Optimal Low Thrust Trajectories to the Moon. 516. 143. 1 indexed citations
11.
Betts, John T., et al.. (2002). Convergence of Nonconvergent IRK Discretizations of Optimal Control Problems with State Inequality Constraints. SIAM Journal on Scientific Computing. 23(6). 1981–2007. 31 indexed citations
12.
Betts, John T., et al.. (2002). Compensating for order variation in mesh refinement for direct transcription methods II: computational experience. Journal of Computational and Applied Mathematics. 143(2). 237–261. 9 indexed citations
13.
Betts, John T.. (2000). Very low-thrust trajectory optimization using a direct SQP method. Journal of Computational and Applied Mathematics. 120(1-2). 27–40. 117 indexed citations
14.
Betts, John T., et al.. (2000). Compensating for order variation in mesh refinement for direct transcription methods. Journal of Computational and Applied Mathematics. 125(1-2). 147–158. 26 indexed citations
15.
Betts, John T.. (1998). Survey of Numerical Methods for Trajectory Optimization. Journal of Guidance Control and Dynamics. 21(2). 193–207. 1860 indexed citations breakdown →
16.
Betts, John T.. (1990). Sparse Jacobian updates in the collocation method for optimal control problems. Journal of Guidance Control and Dynamics. 13(3). 409–415. 20 indexed citations
17.
Betts, John T.. (1988). The application of sparse Broyden updates in the collocation method for optimal control problems. Guidance, Navigation and Control Conference. 1 indexed citations
18.
Betts, John T., et al.. (1984). Solving the optimal control problem using a nonlinear programming technique. I - General formulation. Astrodynamics Conference. 8 indexed citations
19.
Betts, John T.. (1980). A Compact Algorithm for Computing the Stationary Point of a Quadratic Function Subject to Linear Constraints. ACM Transactions on Mathematical Software. 6(3). 391–397. 6 indexed citations
20.
Betts, John T. & Stephen Citron. (1972). Approximate optimal control of distributed parameter systems.. AIAA Journal. 10(1). 19–23. 11 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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