Optimal approximation with sparsely connected deep neural networks H Bolcskei, P Grohs, G Kutyniok, P Petersen SIAM Journal on Mathematics of Data Science 1 (1), 8-45, 2019 | 160 | 2019 |

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 Memoirs of the American Mathematical Society, 2018 | 96 | 2018 |

Parabolic molecules P Grohs, G Kutyniok Foundations of Computational Mathematics 14 (2), 299-337, 2014 | 81 | 2014 |

Laguerre minimal surfaces, isotropic geometry and linear elasticity H Pottmann, P Grohs, NJ Mitra Advances in computational mathematics 31 (4), 391, 2009 | 78 | 2009 |

Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of … J Berner, P Grohs, A Jentzen SIAM Journal on Mathematics of Data Science 2 (3), 631-657, 2020 | 76 | 2020 |

Solving stochastic differential equations and Kolmogorov equations by means of deep learning C Beck, S Becker, P Grohs, N Jaafari, A Jentzen arXiv preprint arXiv:1806.00421, 2018 | 71 | 2018 |

Deep neural network approximation theory D Elbrächter, D Perekrestenko, P Grohs, H Bölcskei IEEE Transactions on Information Theory, 2019 | 67 | 2019 |

Continuous shearlet frames and resolution of the wavefront set P Grohs Monatshefte für Mathematik 164 (4), 393-426, 2011 | 64 | 2011 |

Smoothness properties of Lie group subdivision schemes J Wallner, EN Yazdani, P Grohs Multiscale modeling & simulation 6 (2), 493-505, 2007 | 48 | 2007 |

DNN expression rate analysis of high-dimensional PDEs: Application to option pricing D Elbrächter, P Grohs, A Jentzen, C Schwab arXiv preprint arXiv:1809.07669, 2018 | 45 | 2018 |

*ε*-subgradient algorithms for locally lipschitz functions on Riemannian manifoldsP Grohs, S Hosseini Advances in Computational Mathematics 42 (2), 333-360, 2016 | 45 | 2016 |

A general proximity analysis of nonlinear subdivision schemes P Grohs SIAM Journal on Mathematical Analysis 42 (2), 729-750, 2010 | 44 | 2010 |

Stable phase retrieval in infinite dimensions R Alaifari, I Daubechies, P Grohs, R Yin Foundations of Computational Mathematics 19 (4), 869-900, 2019 | 43 | 2019 |

Optimal a priori discretization error bounds for geodesic finite elements P Grohs, H Hardering, O Sander Foundations of Computational Mathematics 15 (6), 1357-1411, 2015 | 38 | 2015 |

Interpolatory wavelets for manifold-valued data P Grohs, J Wallner Applied and Computational Harmonic Analysis 27 (3), 325-333, 2009 | 37 | 2009 |

Smoothness analysis of subdivision schemes on regular grids by proximity P Grohs SIAM journal on numerical analysis 46 (4), 2169-2182, 2008 | 36 | 2008 |

Phase retrieval in the general setting of continuous frames for Banach spaces R Alaifari, P Grohs SIAM journal on mathematical analysis 49 (3), 1895-1911, 2017 | 31 | 2017 |

α-molecules P Grohs, S Keiper, G Kutyniok, M Schäfer Applied and Computational Harmonic Analysis 41 (1), 297-336, 2016 | 30 | 2016 |

Smoothness equivalence properties of univariate subdivision schemes and their projection analogues P Grohs Numerische Mathematik 113 (2), 163-180, 2009 | 30 | 2009 |

Continuous shearlet tight frames P Grohs Journal of Fourier Analysis and Applications 17 (3), 506-518, 2011 | 29 | 2011 |