Adam M. Oberman

2.6k total citations
45 papers, 1.3k citations indexed

About

Adam M. Oberman is a scholar working on Computational Mechanics, Computational Theory and Mathematics and Applied Mathematics. According to data from OpenAlex, Adam M. Oberman has authored 45 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computational Mechanics, 18 papers in Computational Theory and Mathematics and 13 papers in Applied Mathematics. Recurrent topics in Adam M. Oberman's work include Advanced Mathematical Modeling in Engineering (14 papers), Advanced Numerical Methods in Computational Mathematics (11 papers) and Nonlinear Partial Differential Equations (9 papers). Adam M. Oberman is often cited by papers focused on Advanced Mathematical Modeling in Engineering (14 papers), Advanced Numerical Methods in Computational Mathematics (11 papers) and Nonlinear Partial Differential Equations (9 papers). Adam M. Oberman collaborates with scholars based in Canada, United States and France. Adam M. Oberman's 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 Guillaume Carlier and has published in prestigious journals such as Journal of Computational Physics, Mathematics of Computation and SIAM Journal on Numerical Analysis.

In The Last Decade

Adam M. Oberman

42 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Adam M. Oberman Canada 20 526 405 355 254 182 45 1.3k
Steve Abbott United States 10 371 0.7× 368 0.9× 240 0.7× 132 0.5× 278 1.5× 49 1.3k
Len Bos Canada 21 594 1.1× 321 0.8× 535 1.5× 281 1.1× 107 0.6× 89 1.2k
Gerhard Schmeißer Germany 20 1.2k 2.3× 427 1.1× 185 0.5× 544 2.1× 281 1.5× 82 2.1k
М. К. Керимов Russia 17 401 0.8× 366 0.9× 167 0.5× 384 1.5× 268 1.5× 90 1.8k
Harold R. Parks United States 12 431 0.8× 294 0.7× 161 0.5× 135 0.5× 295 1.6× 37 1.3k
Dejan Slepčev United States 19 380 0.7× 276 0.7× 228 0.6× 43 0.2× 162 0.9× 43 1.3k
Giuseppe Buttazzo Italy 15 420 0.8× 461 1.1× 115 0.3× 87 0.3× 197 1.1× 42 1.2k
Wilfrid Gangbo United States 23 1.3k 2.5× 456 1.1× 171 0.5× 62 0.2× 332 1.8× 54 2.0k
Marco Vianello Italy 18 486 0.9× 364 0.9× 498 1.4× 374 1.5× 68 0.4× 110 1.2k
L. C. Evans United States 11 750 1.4× 463 1.1× 263 0.7× 61 0.2× 254 1.4× 16 1.3k

Countries citing papers authored by Adam M. Oberman

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Bengio, Yoshua, Michael K. Cohen, Joumana Ghosn, et al.. (2025). Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?. 2(5). 1 indexed citations
2.
Oberman, Adam M., et al.. (2023). EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models. SN Computer Science. 4(5). 2 indexed citations
3.
Jacobsen, Jörn-Henrik, et al.. (2020). How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. International Conference on Machine Learning. 1. 3154–3164. 7 indexed citations
4.
Oberman, Adam M., et al.. (2019). Empirical confidence estimates for classification by deep neural networks. arXiv (Cornell University). 2 indexed citations
5.
Oberman, Adam M. & Jeff Calder. (2018). Lipschitz regularized Deep Neural Networks converge and generalize. arXiv (Cornell University). 13 indexed citations
6.
Yin, Penghang, Minh Phạm, Adam M. Oberman, & Stanley Osher. (2018). Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-Means Clustering. Journal of Scientific Computing. 77(2). 1133–1146. 12 indexed citations
7.
Chaudhari, Pratik, Adam M. Oberman, Stanley Osher, Stefano Soatto, & Guillaume Carlier. (2017). Partial differential equations for training deep neural networks. 1627–1631. 7 indexed citations
8.
Carlier, Guillaume, Adam M. Oberman, & Édouard Oudet. (2015). Numerical methods for matching for teams and Wasserstein barycenters. ESAIM Mathematical Modelling and Numerical Analysis. 49(6). 1621–1642. 39 indexed citations
9.
Benamou, Jean‐David, Brittany D. Froese, & Adam M. Oberman. (2013). Numerical solution of the Optimal Transportation problem using the Monge–Ampère equation. Journal of Computational Physics. 260. 107–126. 106 indexed citations
10.
Oberman, Adam M.. (2013). A Numerical Method for Variational Problems with Convexity Constraints. SIAM Journal on Scientific Computing. 35(1). A378–A396. 11 indexed citations
11.
Oberman, Adam M., et al.. (2011). Numerical methods for anisotropic mean curvature flow based on a discrete time variational formulation. Communications in Mathematical Sciences. 9(3). 637–662. 11 indexed citations
12.
Froese, Brittany D. & Adam M. Oberman. (2011). Convergent Finite Difference Solvers for Viscosity Solutions of the Elliptic Monge–Ampère Equation in Dimensions Two and Higher. SIAM Journal on Numerical Analysis. 49(4). 1692–1714. 69 indexed citations
13.
Froese, Brittany D. & Adam M. Oberman. (2010). Fast finite difference solvers for singular solutions of the elliptic Monge–Ampère equation. Journal of Computational Physics. 230(3). 818–834. 44 indexed citations
14.
Benamou, Jean‐David, Brittany D. Froese, & Adam M. Oberman. (2010). Two Numerical Methods for the elliptic Monge-Ampère equation. ESAIM Mathematical Modelling and Numerical Analysis. 44(4). 737–758. 71 indexed citations
15.
Oberman, Adam M.. (2008). An explicit solution of the Lipschitz extension problem. Proceedings of the American Mathematical Society. 136(12). 4329–4338. 4 indexed citations
16.
Oberman, Adam M.. (2006). Convergent Difference Schemes for Degenerate Elliptic and Parabolic Equations: Hamilton--Jacobi Equations and Free Boundary Problems. SIAM Journal on Numerical Analysis. 44(2). 879–895. 120 indexed citations
17.
Oberman, Adam M.. (2004). A convergent difference scheme for the infinity Laplacian: construction of absolutely minimizing Lipschitz extensions. Mathematics of Computation. 74(251). 1217–1231. 74 indexed citations
18.
Gomes, Diogo A. & Adam M. Oberman. (2004). Computing the Effective Hamiltonian Using a Variational Approach. SIAM Journal on Control and Optimization. 43(3). 792–812. 31 indexed citations
19.
Oberman, Adam M. & Thaleia Zariphopoulou. (2003). Pricing early exercise contracts in incomplete markets. Computational Management Science. 1(1). 28 indexed citations
20.
Constantin, Peter, Alexander Kiselev, Adam M. Oberman, & Leonid Ryzhik. (2000). Bulk Burning Rate in¶Passive–Reactive Diffusion. Archive for Rational Mechanics and Analysis. 154(1). 53–91. 91 indexed citations

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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