Stephan Wojtowytsch

405 total citations
10 papers, 119 citations indexed

About

Stephan Wojtowytsch is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Theory and Mathematics. According to data from OpenAlex, Stephan Wojtowytsch has authored 10 papers receiving a total of 119 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Statistical and Nonlinear Physics and 3 papers in Computational Theory and Mathematics. Recurrent topics in Stephan Wojtowytsch's work include Neural Networks and Applications (4 papers), Model Reduction and Neural Networks (4 papers) and Stochastic Gradient Optimization Techniques (3 papers). Stephan Wojtowytsch is often cited by papers focused on Neural Networks and Applications (4 papers), Model Reduction and Neural Networks (4 papers) and Stochastic Gradient Optimization Techniques (3 papers). Stephan Wojtowytsch collaborates with scholars based in United States and Germany. Stephan Wojtowytsch's co-authors include E Weinan, Lei Wu, Benedikt Wirth, Patrick Dondl, Matteo Novaga and Jonathan W. Siegel and has published in prestigious journals such as SIAM Journal on Mathematical Analysis, Calculus of Variations and Partial Differential Equations and Journal of Nonlinear Science.

In The Last Decade

Stephan Wojtowytsch

9 papers receiving 117 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stephan Wojtowytsch United States 5 58 41 19 18 11 10 119
Boris Hanin United States 5 56 1.0× 29 0.7× 27 1.4× 21 1.2× 10 0.9× 9 159
Jason M. Klusowski United States 7 81 1.4× 40 1.0× 28 1.5× 27 1.5× 8 0.7× 15 131
Felix Voigtlaender Germany 7 72 1.2× 48 1.2× 42 2.2× 24 1.3× 11 1.0× 29 182
Phan-Minh Nguyen United States 4 159 2.7× 78 1.9× 39 2.1× 36 2.0× 14 1.3× 7 248
Debarghya Ghoshdastidar India 7 60 1.0× 69 1.7× 28 1.5× 20 1.1× 20 1.8× 15 160
Olivier Ruatta France 6 57 1.0× 14 0.3× 9 0.5× 20 1.1× 44 4.0× 13 116
Alessandro Rudi France 8 73 1.3× 13 0.3× 44 2.3× 46 2.6× 24 2.2× 24 167
Motonobu Kanagawa Japan 6 72 1.2× 17 0.4× 6 0.3× 5 0.3× 14 1.3× 12 131
Chris Junchi Li United States 6 85 1.5× 7 0.2× 16 0.8× 57 3.2× 11 1.0× 14 119
Mert Gürbüzbalaban United States 7 58 1.0× 30 0.7× 11 0.6× 54 3.0× 48 4.4× 31 174

Countries citing papers authored by Stephan Wojtowytsch

Since Specialization
Citations

This map shows the geographic impact of Stephan Wojtowytsch'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 Stephan Wojtowytsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephan Wojtowytsch more than expected).

Fields of papers citing papers by Stephan Wojtowytsch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephan Wojtowytsch. 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 Stephan Wojtowytsch. The network helps show where Stephan Wojtowytsch may publish in the future.

Co-authorship network of co-authors of Stephan Wojtowytsch

This figure shows the co-authorship network connecting the top 25 collaborators of Stephan Wojtowytsch. A scholar is included among the top collaborators of Stephan Wojtowytsch 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 Stephan Wojtowytsch. Stephan Wojtowytsch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Siegel, Jonathan W., et al.. (2024). Nesterov acceleration despite very noisy gradients. 20694–20744.
2.
Wojtowytsch, Stephan. (2023). Stochastic Gradient Descent with Noise of Machine Learning Type Part I: Discrete Time Analysis. Journal of Nonlinear Science. 33(3). 14 indexed citations
3.
Wojtowytsch, Stephan. (2023). Stochastic Gradient Descent with Noise of Machine Learning Type Part II: Continuous Time Analysis. Journal of Nonlinear Science. 34(1). 3 indexed citations
4.
Weinan, E & Stephan Wojtowytsch. (2022). Representation formulas and pointwise properties for Barron functions. Calculus of Variations and Partial Differential Equations. 61(2). 17 indexed citations
5.
Dondl, Patrick & Stephan Wojtowytsch. (2021). Keeping it together: A phase-field version of path-connectedness and its implementation. Journal of Algorithms & Computational Technology. 15. 1 indexed citations
6.
Weinan, E & Stephan Wojtowytsch. (2021). Kolmogorov width decay and poor approximators in machine learning: shallow neural networks, random feature models and neural tangent kernels. Research in the Mathematical Sciences. 8(1). 14 indexed citations
7.
Weinan, E & Stephan Wojtowytsch. (2020). Some observations on partial differential equations in Barron and multi-layer spaces.. arXiv (Cornell University). 2 indexed citations
8.
Wojtowytsch, Stephan & E Weinan. (2020). Can Shallow Neural Networks Beat the Curse of Dimensionality? A Mean Field Training Perspective. IEEE Transactions on Artificial Intelligence. 1(2). 121–129. 22 indexed citations
9.
10.
Dondl, Patrick, Matteo Novaga, Benedikt Wirth, & Stephan Wojtowytsch. (2019). A Phase-field Approximation of the Perimeter under a Connectedness Constraint. SIAM Journal on Mathematical Analysis. 51(5). 3902–3920. 2 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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2026