Carl Leake
Impact in
-
- Model Reduction and Neural Networks
- Modeling and Simulation top 5%
- Fractional Differential Equations Solutions
Papers in
-
- Robotic Mechanisms and Dynamics 2
- Dynamics and Control of Mechanical Systems 2
-
- Model Reduction and Neural Networks 3
- Co-authors
- Daniele Mortari (9 shared papers)Hunter Johnston (6 shared papers)Roberto Furfaro (1 shared paper)Mario De Florio (1 shared paper)Enrico Schiassi (1 shared paper)J. N. Reddy (1 shared paper)Yalchin Efendiev (1 shared paper)Abhinandan Jain (4 shared papers)
- Journals
- Journal of Aircraft (1 paper)Engineering Analysis with Boundary Elements (1 paper)Applied Mathematics and Computation (1 paper)Neurocomputing (1 paper)Mathematics (3 papers)
- Partner nations
- United StatesBrazilSpain
In The Last Decade
Carl Leake
12 papers receiving 248 citations
Peers
Comparison fields: 5 of 39
- Statistical and Nonlinear Physics 125
- Modeling and Simulation 40
- Numerical Analysis 46
- Statistics, Probability and Uncertainty 27
- Aerospace Engineering 65
Countries citing papers authored by Carl Leake
This map shows the geographic impact of Carl Leake'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 Carl Leake with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carl Leake more than expected).
Fields of papers citing papers by Carl Leake
This network shows the impact of papers produced by Carl Leake. 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 Carl Leake. The network helps show where Carl Leake may publish in the future.
Co-authors
The 22 scholars most cited alongside Carl Leake, 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 | 2021 | 128 | |
| 2 | 2020 | 33 | |
| 3 | 2019 | 29 | |
| 4 | 2019 | 17 | |
| 5 | 2021 | 15 | |
| 6 | 2021 | 13 | |
| 7 | 2019 | 10 | |
| 8 | 2020 | 4 | |
| 9 | 2023 | 3 | |
| 10 | 2024 | 2 | |
| 11 | 2024 | 1 | |
| 12 | Theory of Connections Applied to Support Vector Machines to Solve Differential Equations. | 2018 | 1 |
| 13 | 2025 | 0 | |
| 14 | 2025 | 0 |
About Carl Leake
Carl Leake is a scholar working on Control and Systems Engineering, Statistical and Nonlinear Physics, Aerospace Engineering, Mechanical Engineering and Numerical Analysis, having authored 14 papers that have together received 256 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (3 papers), Spacecraft Dynamics and Control (2 papers), Robotic Mechanisms and Dynamics (2 papers), Aerospace Engineering and Energy Systems (2 papers), Numerical methods for differential equations (2 papers), Probabilistic and Robust Engineering Design (2 papers), Dynamics and Control of Mechanical Systems (2 papers) and Data Management and Algorithms (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (125 citations), Modeling and Simulation (40 citations), Numerical Analysis (46 citations), Statistics, Probability and Uncertainty (27 citations) and Aerospace Engineering (65 citations). Carl Leake has collaborated with scholars based in United States, Brazil and Spain. Frequent co-authors include Daniele Mortari, Hunter Johnston, Roberto Furfaro, Mario De Florio, Enrico Schiassi, J. N. Reddy, Yalchin Efendiev, Abhinandan Jain, Håvard Fjær Grip and Martin R. Cacan. Their work appears in journals such as Journal of Aircraft, Engineering Analysis with Boundary Elements, Applied Mathematics and Computation, Neurocomputing and 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.