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 American Mathematical Society 284 (1410), 2023 | 279 | 2023 |

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 | 143 | 2020 |

Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks M Hutzenthaler, A Jentzen, W Wurstemberger | 62 | 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 | 57 | 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 | 56 | 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 | 40 | 2020 |

Mathematical introduction to deep learning: methods, implementations, and theory A Jentzen, B Kuckuck, P von Wurstemberger arXiv preprint arXiv:2310.20360, 2023 | 16 | 2023 |

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 Mathematical Finance 34 (1), 90-150, 2024 | 15 | 2024 |

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 | 13 | 2020 |

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 | 3 | 2023 |

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning L Gonon, A Jentzen, B Kuckuck, S Liang, A Riekert, P von Wurstemberger arXiv preprint arXiv:2408.13222, 2024 | | 2024 |

A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant … P Grohs, F Hornung, A Jentzen, P von Wurstemberger | | 2021 |

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 |