| Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions P Grohs, L Herrmann IMA Journal of Numerical Analysis 42 (3), 2055-2082, 2022 | 65 | 2022 |
| 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 | 48 | 2018 |
| Deep neural network expression of posterior expectations in Bayesian PDE inversion L Herrmann, C Schwab, J Zech Inverse Problems 36 (12), 125011, 2020 | 47* | 2020 |
| Constructive deep ReLU neural network approximation L Herrmann, JAA Opschoor, C Schwab Journal of Scientific Computing 90 (2), 75, 2022 | 42 | 2022 |
| 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 | 37 | 2019 |
| QMC integration for lognormal-parametric, elliptic PDEs: local supports and product weights L Herrmann, C Schwab Numerische Mathematik 141, 63-102, 2019 | 33* | 2019 |
| 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 | 30 | 2020 |
| Numerical analysis of lognormal diffusions on the sphere L Herrmann, A Lang, C Schwab Stochastics and Partial Differential Equations: Analysis and Computations 6 …, 2018 | 25 | 2018 |
| 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 | 24 | 2021 |
| 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 | 22 | 2018 |
| Neural and gpc operator surrogates: construction and expression rate bounds L Herrmann, C Schwab, J Zech arXiv preprint arXiv:2207.04950 40, 1513-1545, 2022 | 16 | 2022 |
| Multilevel quasi-Monte Carlo uncertainty quantification for advection-diffusion-reaction L Herrmann, C Schwab International Conference on Monte Carlo and Quasi-Monte Carlo Methods in …, 2018 | 15* | 2018 |
| Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations P Grohs, L Herrmann arXiv preprint arXiv:2103.05744, 2021 | 11 | 2021 |
| Neural and spectral operator surrogates: unified construction and expression rate bounds L Herrmann, C Schwab, J Zech Advances in Computational Mathematics 50 (4), 72, 2024 | 10 | 2024 |
| Multilevel approximation of Gaussian random fields: Covariance compression, estimation, and spatial prediction H Harbrecht, L Herrmann, K Kirchner, C Schwab Advances in Computational Mathematics 50 (5), 101, 2024 | 9 | 2024 |
| Strong convergence analysis of iterative solvers for random operator equations L Herrmann Calcolo 56 (4), 46, 2019 | 9 | 2019 |
| QMC algorithms with product weights for lognormal-parametric, elliptic PDEs L Herrmann, C Schwab Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2016, Stanford, CA, August …, 2018 | 8* | 2018 |
| FakET: Simulating cryo-electron tomograms with neural style transfer P Harar, L Herrmann, P Grohs, D Haselbach Structure, 2023 | 5 | 2023 |
| 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 | 5 | 2019 |
| Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data G Schneckenreither, L Herrmann, R Reisenhofer, N Popper, P Grohs Plos one 18 (5), e0286012, 2023 | 4 | 2023 |
Christoph SchwabProfessor for Mathematics, ETH ZurichDirección de correo verificada de math.ethz.ch
