Arnulf Jentzen

7.9k total citations · 2 hit papers
107 papers, 4.0k citations indexed

About

Arnulf Jentzen is a scholar working on Finance, Statistical and Nonlinear Physics and Artificial Intelligence. According to data from OpenAlex, Arnulf Jentzen has authored 107 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Finance, 27 papers in Statistical and Nonlinear Physics and 26 papers in Artificial Intelligence. Recurrent topics in Arnulf Jentzen's work include Stochastic processes and financial applications (66 papers), Model Reduction and Neural Networks (25 papers) and Advanced Mathematical Modeling in Engineering (20 papers). Arnulf Jentzen is often cited by papers focused on Stochastic processes and financial applications (66 papers), Model Reduction and Neural Networks (25 papers) and Advanced Mathematical Modeling in Engineering (20 papers). Arnulf Jentzen collaborates with scholars based in Germany, Switzerland and China. Arnulf Jentzen's co-authors include E Weinan, Jiequn Han, Peter E. Kloeden, Martin Hutzenthaler, Christian Beck, S. Becker, Patrick Cheridito, Philipp Grohs, Dirk Blömker and Thomas Kruse and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and IEEE Transactions on Automatic Control.

In The Last Decade

Arnulf Jentzen

101 papers receiving 3.8k citations

Hit Papers

Solving high-dimensional partial differential equations u... 2017 2026 2020 2023 2018 2017 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Solving high-dimensional partial differential equations using deep learning
    2018 996 citations Arnulf Jentzen, E Weinan et al. profile →
  2. Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations
    2017 456 citations E Weinan, Arnulf Jentzen et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arnulf Jentzen Germany 27 1.7k 1.6k 950 603 540 107 4.0k
Denis Talay France 24 1.4k 0.8× 336 0.2× 301 0.3× 162 0.3× 327 0.6× 64 2.6k
Ludwig Arnold Germany 26 1.0k 0.6× 1.6k 1.0× 224 0.2× 202 0.3× 275 0.5× 67 5.4k
Raúl Tempone Saudi Arabia 31 285 0.2× 927 0.6× 1.2k 1.3× 294 0.5× 442 0.8× 137 5.0k
B. L. Rozovskiĭ United States 24 1.4k 0.8× 244 0.2× 260 0.3× 339 0.6× 104 0.2× 71 3.0k
Helge Holden Norway 36 445 0.3× 2.6k 1.6× 688 0.7× 88 0.1× 468 0.9× 145 5.6k
Peter E. Kloeden Germany 39 609 0.4× 853 0.5× 233 0.2× 620 1.0× 426 0.8× 302 6.5k
Michel Loève Canada 9 838 0.5× 617 0.4× 390 0.4× 850 1.4× 204 0.4× 12 5.3k
Michel Ledoux France 30 523 0.3× 260 0.2× 567 0.6× 599 1.0× 217 0.4× 114 4.6k
Moshe Zakai Israel 26 910 0.5× 381 0.2× 211 0.2× 604 1.0× 128 0.2× 90 3.1k
Michel Talagrand France 39 932 0.6× 448 0.3× 611 0.6× 1.2k 2.1× 414 0.8× 237 7.4k

Countries citing papers authored by Arnulf Jentzen

Since Specialization
Citations

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

Fields of papers citing papers by Arnulf Jentzen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arnulf Jentzen

This figure shows the co-authorship network connecting the top 25 collaborators of Arnulf Jentzen. A scholar is included among the top collaborators of Arnulf Jentzen 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 Arnulf Jentzen. Arnulf Jentzen 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.
Beck, Christian, et al.. (2025). Nonlinear Monte Carlo Methods with Polynomial Runtime for Bellman Equations of Discrete Time High-Dimensional Stochastic Optimal Control Problems. Applied Mathematics & Optimization. 91(1). 26–26. 1 indexed citations
2.
Cheridito, Patrick, et al.. (2024). Gradient Descent Provably Escapes Saddle Points in the Training of Shallow ReLU Networks. Journal of Optimization Theory and Applications. 203(3). 2617–2648. 1 indexed citations
3.
Dereich, Steffen, et al.. (2024). On the Existence of Minimizers in Shallow Residual ReLU Neural Network Optimization Landscapes. SIAM Journal on Numerical Analysis. 62(6). 2640–2666. 3 indexed citations
4.
Grohs, Philipp, et al.. (2023). Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. Journal of Complexity. 77. 101746–101746. 5 indexed citations
5.
Grohs, Philipp, et al.. (2023). Space-time error estimates for deep neural network approximations for differential equations. Advances in Computational Mathematics. 49(1). 11 indexed citations
6.
Jentzen, Arnulf, et al.. (2023). Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation. Applied Mathematics and Computation. 455. 127907–127907. 8 indexed citations
7.
Becker, S., et al.. (2023). Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions. Partial Differential Equations and Applications. 4(6). 8 indexed citations
8.
Grohs, Philipp, et al.. (2022). Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms. Partial Differential Equations and Applications. 3(4). 13 indexed citations
9.
10.
Jentzen, Arnulf, et al.. (2017). On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 473(2207). 20170104–20170104. 5 indexed citations
11.
Jentzen, Arnulf, et al.. (2016). On the differentiability of solutions of stochastic evolution equations with respect to their initial values. arXiv (Cornell University). 6 indexed citations
12.
Weinan, E, Martin Hutzenthaler, Arnulf Jentzen, & Thomas Kruse. (2016). Linear scaling algorithms for solving high-dimensional nonlinear parabolic differential equations. arXiv (Cornell University). 3 indexed citations
13.
Becker, S. & Arnulf Jentzen. (2016). Strong convergence rates for nonlinearity-truncated Euler-type approximations of stochastic Ginzburg-Landau equations. arXiv (Cornell University). 31 indexed citations
14.
15.
16.
Jentzen, Arnulf, et al.. (2015). Existence, uniqueness, and regularity for stochastic evolution equations with irregular initial values. arXiv (Cornell University). 9 indexed citations
17.
Conus, Daniel, et al.. (2014). Weak convergence rates of spectral Galerkin approximations for SPDEs with nonlinear diffusion coefficients. arXiv (Cornell University). 26 indexed citations
18.
Hutzenthaler, Martin, Arnulf Jentzen, & Peter E. Kloeden. (2011). Divergence of the multilevel Monte Carlo method. arXiv (Cornell University). 7 indexed citations
19.
Hutzenthaler, Martin & Arnulf Jentzen. (2009). Non-globally Lipschitz Counterexamples for the stochastic Euler scheme. arXiv (Cornell University). 11 indexed citations
20.
Jentzen, Arnulf. (2009). Taylor expansions of solutions of stochastic partial differential equations. arXiv (Cornell University). 6 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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