Milan Korda
- Control and Systems Engineering top 1%
- Statistical and Nonlinear Physics top 1%
- Statistics, Probability and Uncertainty top 1%
- Computational Mechanics top 5%
- Numerical Analysis top 5%
- Co-authors
- Igor MezićDidier HenrionColin N. JonesTimm FaulwasserDominique BonvinJiří CiglerJean B. LasserreFrauke Oldewurtel
- Topics
- Advanced Control Systems Optimization (20 papers)Advanced Optimization Algorithms Research (18 papers)Model Reduction and Neural Networks (11 papers)
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyControl and Systems Engineering
- Partner nations
- FranceCzechiaSwitzerland
In The Last Decade
Milan Korda
36 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 78
- Control and Systems Engineering 848
- Statistical and Nonlinear Physics 706
- Statistics, Probability and Uncertainty 289
- Computational Mechanics 256
- Numerical Analysis 173
Countries citing papers authored by Milan Korda
This map shows the geographic impact of Milan Korda'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 Milan Korda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milan Korda more than expected).
Fields of papers citing papers by Milan Korda
This network shows the impact of papers produced by Milan Korda. 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 Milan Korda. The network helps show where Milan Korda may publish in the future.
Co-authorship network of co-authors of Milan Korda
This figure shows the co-authorship network connecting the top 25 collaborators of Milan Korda. A scholar is included among the top collaborators of Milan Korda 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 Milan Korda. Milan Korda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 5 | |
| 10 | 24 | |
| 11 | Learning Koopman eigenfunctions for prediction and control: the transient case | 3 |
| 12 | 69 | |
| 13 | On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operatorbreakdown → | 216 |
| 14 | 13 | |
| 15 | 3 | |
| 16 | 6 | |
| 17 | 35 | |
| 18 | 31 | |
| 19 | 2 | |
| 20 | Stochastic Model Predictive Control | 1 |
About Milan Korda
Milan Korda is a scholar working on Numerical Analysis, Control and Systems Engineering and Statistical and Nonlinear Physics, having authored 38 papers that have together received 1.5k indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (20 papers), Advanced Optimization Algorithms Research (18 papers) and Model Reduction and Neural Networks (11 papers). The work is most often cited by research in Statistical and Nonlinear Physics (706 citations), Statistics, Probability and Uncertainty (289 citations) and Control and Systems Engineering (848 citations). Milan Korda has collaborated with scholars based in France, Czechia and Switzerland. Frequent co-authors include Igor Mezić, Didier Henrion, Colin N. Jones, Timm Faulwasser, Dominique Bonvin, Jiří Cigler, Jean B. Lasserre, Frauke Oldewurtel, Ravi Gondhalekar and Victor Magron. Their work appears in journals such as IEEE Transactions on Automatic Control, Automatica and Mathematical Programming.
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.