Adam M. Oberman
- Applied Mathematics top 1%
- Computational Theory and Mathematics top 1%
- Computational Mechanics top 5%
- Numerical Analysis top 2%
- Mathematical Physics top 5%
- Co-authors
- Brittany D. FroeseYanghong HuangJean‐David BenamouPeter ConstantinAlexander KiselevLeonid RyzhikLuís SilvestreDiogo A. Gomes
- Topics
- Advanced Mathematical Modeling in Engineering (14 papers)Advanced Numerical Methods in Computational Mathematics (11 papers)Nonlinear Partial Differential Equations (9 papers)
- Journals
- Journal of Computational PhysicsMathematics of ComputationSIAM Journal on Numerical Analysis
- Partner nations
- CanadaUnited StatesFrance
In The Last Decade
Adam M. Oberman
42 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 81
- Applied Mathematics 526
- Computational Theory and Mathematics 405
- Computational Mechanics 355
- Numerical Analysis 254
- Mathematical Physics 182
Countries citing papers authored by Adam M. Oberman
This map shows the geographic impact of Adam M. Oberman'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 Adam M. Oberman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam M. Oberman more than expected).
Fields of papers citing papers by Adam M. Oberman
This network shows the impact of papers produced by Adam M. Oberman. 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 Adam M. Oberman. The network helps show where Adam M. Oberman may publish in the future.
Co-authorship network of co-authors of Adam M. Oberman
This figure shows the co-authorship network connecting the top 25 collaborators of Adam M. Oberman. A scholar is included among the top collaborators of Adam M. Oberman 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 Adam M. Oberman. Adam M. Oberman 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 | How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization | 7 |
| 4 | Empirical confidence estimates for classification by deep neural networks | 2 |
| 5 | Lipschitz regularized Deep Neural Networks converge and generalize | 13 |
| 6 | 7 | |
| 7 | 39 | |
| 8 | 106 | |
| 9 | 11 | |
| 10 | 34 | |
| 11 | 11 | |
| 12 | 69 | |
| 13 | 71 | |
| 14 | 4 | |
| 15 | 47 | |
| 16 | 120 | |
| 17 | 74 | |
| 18 | 31 | |
| 19 | 28 | |
| 20 | 91 |
About Adam M. Oberman
Adam M. Oberman is a scholar working on Numerical Analysis, Applied Mathematics and Computational Theory and Mathematics, having authored 45 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Mathematical Modeling in Engineering (14 papers), Advanced Numerical Methods in Computational Mathematics (11 papers) and Nonlinear Partial Differential Equations (9 papers). The work is most often cited by research in Applied Mathematics (526 citations), Numerical Analysis (254 citations) and Modeling and Simulation (161 citations). Adam M. Oberman has collaborated with scholars based in Canada, United States and France. Frequent co-authors include Brittany D. Froese, Yanghong Huang, Jean‐David Benamou, Peter Constantin, Alexander Kiselev, Leonid Ryzhik, Luís Silvestre, Diogo A. Gomes, Thaleia Zariphopoulou and Édouard Oudet. Their work appears in journals such as Journal of Computational Physics, Mathematics of 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.