Deep ReLU networks and high-order finite element methods JAA Opschoor, PC Petersen, C Schwab Analysis and Applications 18 (05), 715-770, 2020 | 96 | 2020 |

Exponential ReLU DNN expression of holomorphic maps in high dimension JAA Opschoor, C Schwab, J Zech Constructive Approximation 55 (1), 537-582, 2022 | 66 | 2022 |

Exponential relu neural network approximation rates for point and edge singularities C Marcati, JAA Opschoor, PC Petersen, C Schwab Foundations of Computational Mathematics, 1-85, 2022 | 7 | 2022 |

Constructive deep ReLU neural network approximation L Herrmann, JAA Opschoor, C Schwab Journal of Scientific Computing 90 (2), 1-37, 2022 | 7 | 2022 |

Deep learning in high dimension: ReLU network expression rates for bayesian PDE inversion JAA Opschoor, C Schwab, J Zech SAM Research Report 2020, 2020 | 7 | 2020 |

De Rham compatible Deep Neural Networks M Longo, JAA Opschoor, N Disch, C Schwab, J Zech arXiv preprint arXiv:2201.05395, 2022 | 2 | 2022 |

15 Deep learning in high dimension: ReLU neural network expression for Bayesian PDE inversion JAA Opschoor, C Schwab, J Zech Optimization and Control for Partial Differential Equations: Uncertainty …, 2022 | | 2022 |

ReLU DNN expression of sparse gpc expansion in uncertainty quantification JAA Opschoor, C Schwab | | |

Exponential Deep Neural Network Expression for Solution Sets of PDEs Christoph Schwab Seminar for Applied Mathematics ETH Zürich, Switzerland J Opschoor, CM ETH, L Gonon, L Herrmann, P Petersen, J Zech | | |