Nikola Kovachki
- Statistical and Nonlinear Physics top 5%
- Artificial Intelligence top 10%
- Computational Mechanics top 10%
- Ocean Engineering top 10%
- Mechanics of Materials
- Co-authors
- Andrew M. StuartKamyar AzizzadenesheliAnima AnandkumarZongyi LiBurigede LiuKaushik BhattacharyaJean KossaifiDavid Jin
- Topics
- Model Reduction and Neural Networks (5 papers)Neural Networks and Applications (4 papers)Bayesian Methods and Mixture Models (2 papers)
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyComputational Mechanics
- Journals
- Mathematics of ComputationJournal of Chemical Theory and ComputationSIAM Journal on Numerical Analysis
- Partner nations
- United StatesIsrael
In The Last Decade
Nikola Kovachki
12 papers receiving 495 citations
Hit Papers
Peers
Comparison fields: 5 of 71
- Statistical and Nonlinear Physics 184
- Artificial Intelligence 137
- Computational Mechanics 97
- Ocean Engineering 63
- Mechanics of Materials 54
Countries citing papers authored by Nikola Kovachki
This map shows the geographic impact of Nikola Kovachki'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 Nikola Kovachki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikola Kovachki more than expected).
Fields of papers citing papers by Nikola Kovachki
This network shows the impact of papers produced by Nikola Kovachki. 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 Nikola Kovachki. The network helps show where Nikola Kovachki may publish in the future.
Co-authorship network of co-authors of Nikola Kovachki
This figure shows the co-authorship network connecting the top 25 collaborators of Nikola Kovachki. A scholar is included among the top collaborators of Nikola Kovachki 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 Nikola Kovachki. Nikola Kovachki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 2 | |
| 3 | Neural operators for accelerating scientific simulations and designbreakdown → | 90 |
| 4 | 6 | |
| 5 | Physics-Informed Neural Operator for Learning Partial Differential Equationsbreakdown → | 103 |
| 6 | 14 | |
| 7 | Fourier Neural Operator for Parametric Partial Differential Equations | 123 |
| 8 | 46 | |
| 9 | Multipole Graph Neural Operator for Parametric Partial Differential Equations | 8 |
| 10 | 73 | |
| 11 | 46 | |
| 12 | 4 |
About Nikola Kovachki
Nikola Kovachki is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Statistics and Probability, having authored 12 papers that have together received 518 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (5 papers), Neural Networks and Applications (4 papers) and Bayesian Methods and Mixture Models (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (184 citations), Statistics, Probability and Uncertainty (35 citations) and Computational Mechanics (97 citations). Nikola Kovachki has collaborated with scholars based in United States and Israel. Frequent co-authors include Andrew M. Stuart, Kamyar Azizzadenesheli, Anima Anandkumar, Zongyi Li, Burigede Liu, Kaushik Bhattacharya, Jean Kossaifi, David Jin, Hongkai Zheng and Matthew Welborn. Their work appears in journals such as Mathematics of Computation, Journal of Chemical Theory and Computation and SIAM Journal on Numerical Analysis.
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.