Lukas Gonon
- Finance top 5%
- Artificial Intelligence top 10%
- Management Science and Operations Research top 5%
- Economics and Econometrics top 10%
- Electrical and Electronic Engineering
- Co-authors
- Hans BuehlerBen WoodJosef TeichmannLyudmila GrigoryevaJuan‐Pablo OrtegaChristoph SchwabAmanda ProrokAlcherio Martinoli
- Topics
- Stochastic processes and financial applications (10 papers)Neural Networks and Applications (6 papers)Model Reduction and Neural Networks (5 papers)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsNeural NetworksPhysica D Nonlinear Phenomena
- Partner nations
- SwitzerlandUnited KingdomGermany
In The Last Decade
Lukas Gonon
19 papers receiving 319 citations
Peers
Comparison fields: 5 of 47
- Finance 144
- Artificial Intelligence 112
- Management Science and Operations Research 110
- Economics and Econometrics 69
- Electrical and Electronic Engineering 67
Countries citing papers authored by Lukas Gonon
This map shows the geographic impact of Lukas Gonon'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 Lukas Gonon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lukas Gonon more than expected).
Fields of papers citing papers by Lukas Gonon
This network shows the impact of papers produced by Lukas Gonon. 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 Lukas Gonon. The network helps show where Lukas Gonon may publish in the future.
Co-authorship network of co-authors of Lukas Gonon
This figure shows the co-authorship network connecting the top 25 collaborators of Lukas Gonon. A scholar is included among the top collaborators of Lukas Gonon 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 Lukas Gonon. Lukas Gonon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 0 | |
| 9 | 27 | |
| 10 | 21 | |
| 11 | Deep ReLU Neural Network Approximation for Stochastic Differential Equations with Jumps. | 5 |
| 12 | 20 | |
| 13 | Expressive Power of Randomized Signature | 4 |
| 14 | Risk Bounds for Reservoir Computing | 3 |
| 15 | 3 | |
| 16 | 15 | |
| 17 | 27 | |
| 18 | 148 | |
| 19 | 12 | |
| 20 | On Skorokhod embeddings and Poisson equations | 2 |
About Lukas Gonon
Lukas Gonon is a scholar working on Finance, Statistical and Nonlinear Physics and Management Science and Operations Research, having authored 23 papers that have together received 336 indexed citations. Recurring topics across this work include Stochastic processes and financial applications (10 papers), Neural Networks and Applications (6 papers) and Model Reduction and Neural Networks (5 papers). The work is most often cited by research in Finance (144 citations), Management Science and Operations Research (110 citations) and Statistical and Nonlinear Physics (62 citations). Lukas Gonon has collaborated with scholars based in Switzerland, United Kingdom and Germany. Frequent co-authors include Hans Buehler, Ben Wood, Josef Teichmann, Lyudmila Grigoryeva, Juan‐Pablo Ortega, Christoph Schwab, Amanda Prorok, Alcherio Martinoli, Arnulf Jentzen and Philipp Grohs. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Neural Networks and Physica D Nonlinear Phenomena.
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.