| Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ C Schwab, J Zech Analysis and Applications, 1-37, 2018 | 228 | 2018 |
| Exponential ReLU DNN expression of holomorphic maps in high dimension JAA Opschoor, C Schwab, J Zech Constructive Approximation 55 (1), 537-582, 2022 | 140 | 2022 |
| Shape holomorphy of the stationary Navier--Stokes equations A Cohen, C Schwab, J Zech SIAM Journal on Mathematical Analysis 50 (2), 1720-1752, 2018 | 68 | 2018 |
| Electromagnetic wave scattering by random surfaces: Shape holomorphy C Jerez-Hanckes, C Schwab, J Zech Mathematical Models and Methods in Applied Sciences 27 (12), 2229-2259, 2017 | 64 | 2017 |
| Convergence rates of high dimensional Smolyak quadrature J Zech, C Schwab ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020 | 56 | 2020 |
| Deep neural network expression of posterior expectations in Bayesian PDE inversion L Herrmann, C Schwab, J Zech Inverse Problems 36 (12), 125011, 2020 | 48* | 2020 |
| Multilevel approximation of parametric and stochastic PDEs J Zech, D Dũng, C Schwab Mathematical Models and Methods in Applied Sciences 29 (09), 1753-1817, 2019 | 44 | 2019 |
| Deep learning in high dimension: neural network expression rates for analytic functions in C Schwab, J Zech SIAM/ASA Journal on Uncertainty Quantification 11 (1), 199-234, 2023 | 43 | 2023 |
| 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 | 42* | 2024 |
| Mathematical theory of deep learning P Petersen, J Zech arXiv preprint arXiv:2407.18384, 2024 | 37 | 2024 |
| Sparse-grid approximation of high-dimensional parametric PDEs J Zech ETH Zurich, 2018 | 36* | 2018 |
| Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case J Zech, Y Marzouk Constructive Approximation, 1-68, 2022 | 34* | 2022 |
| Analyticity and sparsity in uncertainty quantification for PDEs with Gaussian random field inputs D Dũng, Van Kien Nguyen, C Schwab, J Zech Springer, 2023 | 31 | 2023 |
| Domain uncertainty quantification in computational electromagnetics R Aylwin, C Jerez-Hanckes, C Schwab, J Zech SIAM/ASA Journal on Uncertainty Quantification 8 (1), 301-341, 2020 | 31 | 2020 |
| Distribution learning via neural differential equations: a nonparametric statistical perspective Y Marzouk, ZR Ren, S Wang, J Zech Journal of Machine Learning Research 25 (232), 1-61, 2024 | 30 | 2024 |
| 15 Deep learning in high dimension: ReLU neural network expression for JAA Opschoor, C Schwab, J Zech Optimization and Control for Partial Differential Equations: Uncertainty …, 2022 | 30* | 2022 |
| Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case J Zech, Y Marzouk Constructive Approximation 55 (3), 987-1036, 2022 | 29 | 2022 |
| De Rham compatible deep neural network FEM M Longo, JAA Opschoor, N Disch, C Schwab, J Zech Neural Networks 165, 721-739, 2023 | 27* | 2023 |
| Deep operator network approximation rates for Lipschitz operators C Schwab, A Stein, J Zech arXiv preprint arXiv:2307.09835, 2023 | 21 | 2023 |
| A Posteriori Error Estimation of - Finite Element Methods for Highly Indefinite Helmholtz Problems S Sauter, J Zech SIAM Journal on Numerical Analysis 53 (5), 2414-2440, 2015 | 20 | 2015 |
Christoph SchwabProfessor for Mathematics, ETH ZurichVerified email at math.ethz.ch
