Quasi--Monte Carlo integration for affine-parametric, elliptic PDEs: Local supports and product weights RN Gantner, L Herrmann, C Schwab SIAM Journal on Numerical Analysis 56 (1), 111-135, 2018 | 34 | 2018 |

Deep neural network expression of posterior expectations in Bayesian PDE inversion L Herrmann, C Schwab, J Zech Inverse Problems 36 (12), 125011, 2020 | 20* | 2020 |

Multilevel quasi-Monte Carlo integration with product weights for elliptic PDEs with lognormal coefficients L Herrmann, C Schwab ESAIM: Mathematical Modelling and Numerical Analysis 53 (5), 1507-1552, 2019 | 19 | 2019 |

QMC integration for lognormal-parametric, elliptic PDEs: local supports and product weights L Herrmann, C Schwab Numerische Mathematik 141 (1), 63-102, 2019 | 19* | 2019 |

Numerical analysis of lognormal diffusions on the sphere L Herrmann, A Lang, C Schwab Stochastics and Partial Differential Equations: Analysis and Computations 6 …, 2018 | 19* | 2018 |

Multilevel QMC with product weights for affine-parametric, elliptic PDEs RN Gantner, L Herrmann, C Schwab Contemporary Computational Mathematics-a celebration of the 80th birthday of …, 2018 | 16* | 2018 |

Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions P Grohs, L Herrmann IMA Journal of Numerical Analysis, 2021 | 13 | 2021 |

Multilevel quasi-Monte Carlo uncertainty quantification for advection-diffusion-reaction L Herrmann, C Schwab Monte Carlo and Quasi-Monte Carlo Methods 324, 31-67, 2020 | 8* | 2020 |

Multilevel approximation of Gaussian random fields: Fast simulation L Herrmann, K Kirchner, C Schwab Mathematical Models and Methods in Applied Sciences 30 (1), 181-223, 2020 | 8 | 2020 |

Strong convergence analysis of iterative solvers for random operator equations L Herrmann Calcolo 56 (4), 1-26, 2019 | 8 | 2019 |

QMC algorithms with product weights for lognormal-parametric, elliptic PDEs L Herrmann, C Schwab International Conference on Monte Carlo and Quasi-Monte Carlo Methods in …, 2016 | 6* | 2016 |

Quasi-Monte Carlo integration in uncertainty quantification for PDEs with log-Gaussian random field inputs L Herrmann ETH Zurich, 2019 | 4 | 2019 |

Quasi-Monte Carlo Bayesian estimation under Besov priors in elliptic inverse problems L Herrmann, M Keller, C Schwab Mathematics of Computation 90 (330), 1831-1860, 2021 | 3 | 2021 |

Uncertainty quantification for spectral fractional diffusion: Sparsity analysis of parametric solutions L Herrmann, C Schwab, J Zech SIAM/ASA Journal on Uncertainty Quantification 7 (3), 913-947, 2019 | 3 | 2019 |

Isotropic random fields on the sphere—Stochastic heat equation and regularity of random elliptic PDEs L Herrmann Master's thesis, ETH Zürich, 2013 | 3* | 2013 |

Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations P Grohs, L Herrmann arXiv preprint arXiv:2103.05744, 2021 | 2 | 2021 |

Constructive deep ReLU neural network approximation L Herrmann, JAA Opschoor, C Schwab 2021‐04, Seminar for Applied Mathematics, 2021 | 2 | 2021 |

Multilevel approximation of Gaussian random fields: Covariance compression, estimation and spatial prediction H Harbrecht, L Herrmann, K Kirchner, C Schwab arXiv preprint arXiv:2103.04424, 2021 | 1 | 2021 |