| A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations P Grohs, F Hornung, A Jentzen, P Von Wurstemberger arXiv preprint arXiv:1809.02362, 2018 | 189 | 2018 |
| Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations M Hutzenthaler, A Jentzen, T Kruse, T Anh Nguyen, P von Wurstemberger Proceedings of the Royal Society A 476 (2244), 20190630, 2020 | 93 | 2020 |
| Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks M Hutzenthaler, A Jentzen, W Wurstemberger | 41 | 2020 |
| Strong error analysis for stochastic gradient descent optimization algorithms A Jentzen, B Kuckuck, A Neufeld, P von Wurstemberger IMA Journal of Numerical Analysis 41 (1), 455-492, 2021 | 36 | 2021 |
| Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates A Jentzen, P von Wurstemberger Journal of Complexity 57, 101438, 2020 | 32 | 2020 |
| Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations S Becker, R Braunwarth, M Hutzenthaler, A Jentzen, ... arXiv preprint arXiv:2005.10206, 2020 | 24 | 2020 |
| High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations P Beneventano, P Cheridito, A Jentzen, P von Wurstemberger arXiv preprint arXiv:2012.04326, 2020 | 8 | 2020 |
| Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing S Becker, A Jentzen, MS Müller, P von Wurstemberger arXiv preprint arXiv:2202.02717, 2022 | 7 | 2022 |
| Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations A Jentzen, A Riekert, P von Wurstemberger arXiv preprint arXiv:2302.03286, 2023 | | 2023 |
| Overcoming the course of dimensionality with DNNs: Theoretical approximation results for PDEs P von Wurstemberger 3rd International Conference on Computational Finance (ICCF2019), 86, 2019 | | 2019 |
Arnulf JentzenThe Chinese University of Hong Kong, Shenzhen & University of MünsterVerified email at uni-muenster.de
