Daniel Jaeggi

409 total citations
8 papers, 239 citations indexed

About

Daniel Jaeggi is a scholar working on Computational Theory and Mathematics, Aerospace Engineering and Mechanical Engineering. According to data from OpenAlex, Daniel Jaeggi has authored 8 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Theory and Mathematics, 3 papers in Aerospace Engineering and 3 papers in Mechanical Engineering. Recurrent topics in Daniel Jaeggi's work include Advanced Multi-Objective Optimization Algorithms (7 papers), Turbomachinery Performance and Optimization (3 papers) and Probabilistic and Robust Engineering Design (2 papers). Daniel Jaeggi is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (7 papers), Turbomachinery Performance and Optimization (3 papers) and Probabilistic and Robust Engineering Design (2 papers). Daniel Jaeggi collaborates with scholars based in United Kingdom, South Sudan and Canada. Daniel Jaeggi's co-authors include P. John Clarkson, Timoleon Kipouros, GT Parks, Geoffrey T. Parks, William N. Dawes, Mark Savill, Jerome P. Jarrett, Tiziano Ghisu, A.T. Bryant and Patrick Palmer and has published in prestigious journals such as European Journal of Operational Research, AIAA Journal and Engineering Optimization.

In The Last Decade

Daniel Jaeggi

8 papers receiving 222 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Jaeggi United Kingdom 7 108 73 43 40 35 8 239
Dipankar Ghosh United States 5 231 2.1× 33 0.5× 129 3.0× 40 1.0× 68 1.9× 10 383
William N. Dawes United Kingdom 13 57 0.5× 179 2.5× 32 0.7× 196 4.9× 83 2.4× 42 377
Lawrence Green United States 11 116 1.1× 157 2.2× 169 3.9× 162 4.0× 32 0.9× 16 425
Ting‐Yu Chen Taiwan 11 107 1.0× 12 0.2× 40 0.9× 19 0.5× 38 1.1× 29 332
Jae-Woo Lee South Korea 10 96 0.9× 160 2.2× 86 2.0× 52 1.3× 20 0.6× 24 341
Kristian Amadori Sweden 13 44 0.4× 161 2.2× 26 0.6× 38 0.9× 82 2.3× 45 430
Arlene A. Moore United States 4 144 1.3× 116 1.6× 80 1.9× 14 0.3× 39 1.1× 8 325
Nhu Van Nguyen South Korea 10 131 1.2× 176 2.4× 117 2.7× 56 1.4× 31 0.9× 33 381
Kurt Hacker United States 10 151 1.4× 38 0.5× 83 1.9× 12 0.3× 68 1.9× 24 366
Wencai Luo China 4 201 1.9× 85 1.2× 285 6.6× 19 0.5× 41 1.2× 10 439

Countries citing papers authored by Daniel Jaeggi

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Jaeggi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Jaeggi. 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 Daniel Jaeggi. The network helps show where Daniel Jaeggi may publish in the future.

Co-authorship network of co-authors of Daniel Jaeggi

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

All Works

8 of 8 papers shown
1.
Ghisu, Tiziano, Geoffrey T. Parks, Daniel Jaeggi, Jerome P. Jarrett, & P. John Clarkson. (2010). The benefits of adaptive parametrization in multi-objective Tabu Search optimization. Engineering Optimization. 42(10). 959–981. 18 indexed citations
2.
Dawes, William N., et al.. (2009). A Practical Demonstration of Scalable, Parallel Mesh Generation. 47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition. 25 indexed citations
3.
Kipouros, Timoleon, Daniel Jaeggi, W. N. Dawes, et al.. (2008). Insight into High-quality Aerodynamic Design Spaces through Multi-objective Optimization. Computer Modeling in Engineering & Sciences. 37(1). 1–44. 14 indexed citations
4.
Jaeggi, Daniel, Geoff Parks, William N. Dawes, & P. John Clarkson. (2008). Robust Multi-Fidelity Aerodynamic Design Optimization Using Surrogate Models. 6 indexed citations
5.
Kipouros, Timoleon, Daniel Jaeggi, William N. Dawes, et al.. (2008). Biobjective Design Optimization for Axial Compressors Using Tabu Search. AIAA Journal. 46(3). 701–711. 50 indexed citations
6.
Jaeggi, Daniel, GT Parks, Timoleon Kipouros, & P. John Clarkson. (2006). The development of a multi-objective Tabu Search algorithm for continuous optimisation problems. European Journal of Operational Research. 185(3). 1192–1212. 113 indexed citations
7.
Bryant, A.T., Daniel Jaeggi, Geoffrey T. Parks, & Patrick Palmer. (2005). The influence of operating conditions on multi-objective optimization of power electronic devices and circuits. Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005.. 2. 1449–1456. 11 indexed citations
8.

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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