| 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 | 149 | 2018 |
| 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 |
| 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 | 43 | 2017 |
| Shape holomorphy of the stationary Navier--Stokes equations A Cohen, C Schwab, J Zech SIAM Journal on Mathematical Analysis 50 (2), 1720-1752, 2018 | 37 | 2018 |
| Convergence rates of high dimensional Smolyak quadrature J Zech, C Schwab ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020 | 35 | 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 | 33 | 2019 |
| Deep neural network expression of posterior expectations in Bayesian PDE inversion L Herrmann, C Schwab, J Zech Inverse Problems 36 (12), 125011, 2020 | 30* | 2020 |
| 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 | 17 | 2020 |
| Sparse-grid approximation of high-dimensional parametric PDEs J Zech ETH Zurich, 2018 | 14* | 2018 |
| Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case J Zech, Y Marzouk Constructive Approximation, 1-68, 2022 | 13* | 2022 |
| 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 | 13 | 2015 |
| 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 | 8* | 2022 |
| Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case J Zech, Y Marzouk Constructive Approximation 55 (3), 987-1036, 2022 | 7 | 2022 |
| Analyticity and sparsity in uncertainty quantification for PDEs with Gaussian random field inputs D Dũng, VK Nguyen, C Schwab, J Zech arXiv preprint arXiv:2201.01912, 2022 | 5 | 2022 |
| 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 |
| De Rham compatible deep neural networks M Longo, JAA Opschoor, N Disch, C Schwab, J Zech arXiv preprint arXiv:2201.05395, 2022 | 4 | 2022 |
| Neural and gpc operator surrogates: construction and expression rate bounds L Herrmann, C Schwab, J Zech arXiv preprint arXiv:2207.04950, 2022 | 3 | 2022 |
| Deep Learning in High Dimension: Neural Network Approximation of Analytic Functions in C Schwab, J Zech arXiv preprint arXiv:2111.07080, 2021 | 2* | 2021 |
| A posteriori error estimation of hp-DG finite element methods for highly indefinite Helmholtz problems J Zech master’s thesis, Inst. f. Mathematik, Unversität Zürich, 2014. http://www …, 2014 | 2 | 2014 |
| Multilevel Domain Uncertainty Quantification in Computational Electromagnetics R Aylwin, C Jerez-Hanckes, C Schwab, J Zech arXiv preprint arXiv:2212.07240, 2022 | | 2022 |
Christoph SchwabProfessor for Mathematics, ETH ZurichVerified email at math.ethz.ch
