Jonathan W. Siegel

551 total citations
21 papers, 234 citations indexed

About

Jonathan W. Siegel is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Mechanics. According to data from OpenAlex, Jonathan W. Siegel has authored 21 papers receiving a total of 234 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 7 papers in Statistical and Nonlinear Physics and 6 papers in Computational Mechanics. Recurrent topics in Jonathan W. Siegel's work include Neural Networks and Applications (9 papers), Model Reduction and Neural Networks (7 papers) and Machine Learning in Materials Science (4 papers). Jonathan W. Siegel is often cited by papers focused on Neural Networks and Applications (9 papers), Model Reduction and Neural Networks (7 papers) and Machine Learning in Materials Science (4 papers). Jonathan W. Siegel collaborates with scholars based in United States and China. Jonathan W. Siegel's co-authors include Jinchao Xu, Jinchao Xu, Zi‐Kui Liu, Wenrui Hao, Qingguo Hong, Heinz Eulau, Ronald DeVore, Russel E. Caflisch, Robert D. Nowak and Jason M. Klusowski and has published in prestigious journals such as Journal of Computational Physics, IEEE Transactions on Information Theory and Neural Networks.

In The Last Decade

Jonathan W. Siegel

15 papers receiving 233 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonathan W. Siegel United States 8 109 103 68 40 32 21 234
Yaohua Zang Germany 3 44 0.4× 194 1.9× 86 1.3× 16 0.4× 54 1.7× 6 263
Andrea D’Ambrosio United States 11 46 0.4× 79 0.8× 24 0.4× 26 0.7× 6 0.2× 34 297
Vitaly Maiorov Israel 7 135 1.2× 35 0.3× 49 0.7× 44 1.1× 7 0.2× 9 232
Felix Voigtlaender Germany 7 72 0.7× 48 0.5× 24 0.4× 42 1.1× 7 0.2× 29 182
David E. Womble United States 8 30 0.3× 20 0.2× 52 0.8× 12 0.3× 24 0.8× 27 265
Zhenyu Zhao China 9 36 0.3× 24 0.2× 22 0.3× 112 2.8× 41 1.3× 30 264
Friedrich Philipp Germany 9 14 0.1× 82 0.8× 36 0.5× 22 0.6× 4 0.1× 35 240
Boris Hanin United States 5 56 0.5× 29 0.3× 21 0.3× 27 0.7× 4 0.1× 9 159
Binbin Hao China 10 14 0.1× 19 0.2× 52 0.8× 66 1.6× 10 0.3× 36 327

Countries citing papers authored by Jonathan W. Siegel

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan W. Siegel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan W. Siegel. 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 Jonathan W. Siegel. The network helps show where Jonathan W. Siegel may publish in the future.

Co-authorship network of co-authors of Jonathan W. Siegel

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan W. Siegel. A scholar is included among the top collaborators of Jonathan W. Siegel 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 Jonathan W. Siegel. Jonathan W. Siegel 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.
DeVore, Ronald, et al.. (2025). Convergence and error control of consistent PINNs for elliptic PDEs. IMA Journal of Numerical Analysis. 46(1). 90–148.
2.
Klusowski, Jason M. & Jonathan W. Siegel. (2025). Sharp Convergence Rates for Matching Pursuit. IEEE Transactions on Information Theory. 71(7). 5556–5569.
3.
Siegel, Jonathan W.. (2025). Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks. Constructive Approximation. 62(2). 441–469.
4.
DeVore, Ronald, et al.. (2024). Weighted variation spaces and approximation by shallow ReLU networks. Applied and Computational Harmonic Analysis. 74. 101713–101713. 1 indexed citations
5.
Siegel, Jonathan W., et al.. (2024). Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures. Computational Materials Science. 247. 113495–113495. 1 indexed citations
6.
Siegel, Jonathan W., et al.. (2024). Nesterov acceleration despite very noisy gradients. 20694–20744.
7.
Siegel, Jonathan W.. (2024). Sharp lower bounds on the manifold widths of Sobolev and Besov spaces. Journal of Complexity. 85. 101884–101884. 1 indexed citations
8.
Siegel, Jonathan W., et al.. (2024). Entropy-based convergence rates of greedy algorithms. Mathematical Models and Methods in Applied Sciences. 34(5). 779–802. 8 indexed citations
9.
Siegel, Jonathan W., et al.. (2023). Greedy training algorithms for neural networks and applications to PDEs. Journal of Computational Physics. 484. 112084–112084. 28 indexed citations
10.
Siegel, Jonathan W. & Jinchao Xu. (2023). Characterization of the Variation Spaces Corresponding to Shallow Neural Networks. Constructive Approximation. 57(3). 1109–1132. 15 indexed citations
11.
Siegel, Jonathan W. & Jinchao Xu. (2022). Optimal Convergence Rates for the Orthogonal Greedy Algorithm. IEEE Transactions on Information Theory. 68(5). 3354–3361. 13 indexed citations
12.
Siegel, Jonathan W. & Jinchao Xu. (2022). Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks. Foundations of Computational Mathematics. 24(2). 481–537. 30 indexed citations
13.
Siegel, Jonathan W., et al.. (2022). Uniform approximation rates and metric entropy of shallow neural networks. Research in the Mathematical Sciences. 9(3). 7 indexed citations
14.
Siegel, Jonathan W., et al.. (2022). Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks. Computational Materials Science. 208. 111254–111254. 23 indexed citations
15.
Siegel, Jonathan W. & Jinchao Xu. (2021). Improved Approximation Properties of Dictionaries and Applications to Neural Networks. arXiv (Cornell University). 2 indexed citations
16.
Siegel, Jonathan W. & Jinchao Xu. (2021). High-order approximation rates for shallow neural networks with cosine and ReLU activation functions. Applied and Computational Harmonic Analysis. 58. 1–26. 25 indexed citations
17.
Siegel, Jonathan W. & Jinchao Xu. (2020). Approximation rates for neural networks with general activation functions. Neural Networks. 128. 313–321. 69 indexed citations
18.
Siegel, Jonathan W., et al.. (2020). Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability Employing Neural Networks. SSRN Electronic Journal. 5 indexed citations
19.
Caflisch, Russel E., et al.. (2020). Accuracy, Efficiency and Optimization of Signal Fragmentation. Multiscale Modeling and Simulation. 18(2). 737–757.
20.
Siegel, Jonathan W.. (2018). Accelerated First-Order Optimization with Orthogonality Constraints. eScholarship (California Digital Library).

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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