| 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 | 196 | 2018 |
| Exponential ReLU DNN expression of holomorphic maps in high dimension JAA Opschoor, C Schwab, J Zech Constructive Approximation 55 (1), 537-582, 2022 | 110 | 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 | 55 | 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 | 53 | 2017 |
| Convergence rates of high dimensional Smolyak quadrature J Zech, C Schwab ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020 | 48 | 2020 |
| Deep neural network expression of posterior expectations in Bayesian PDE inversion L Herrmann, C Schwab, J Zech Inverse Problems 36 (12), 125011, 2020 | 41* | 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 | 40 | 2019 |
| Sparse-grid approximation of high-dimensional parametric PDEs J Zech ETH Zurich, 2018 | 25* | 2018 |
| Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case J Zech, Y Marzouk Constructive Approximation, 1-68, 2022 | 24* | 2022 |
| 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 | 22 | 2020 |
| 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 | 19* | 2023 |
| 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 | 17* | 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 | 17 | 2015 |
| De Rham compatible deep neural network FEM M Longo, JAA Opschoor, N Disch, C Schwab, J Zech Neural Networks 165, 721-739, 2023 | 16 | 2023 |
| Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case J Zech, Y Marzouk Constructive Approximation 55 (3), 987-1036, 2022 | 16 | 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 | 16 | 2022 |
| Neural and gpc operator surrogates: construction and expression rate bounds L Herrmann, C Schwab, J Zech arXiv preprint arXiv:2207.04950, 2022 | 10 | 2022 |
| Distribution learning via neural differential equations: a nonparametric statistical perspective Y Marzouk, Z Ren, S Wang, J Zech arXiv preprint arXiv:2309.01043, 2023 | 7 | 2023 |
| Deep operator network approximation rates for Lipschitz operators C Schwab, A Stein, J Zech arXiv preprint arXiv:2307.09835, 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 |
Christoph SchwabProfessor for Mathematics, ETH Zurich在 math.ethz.ch 的电子邮件经过验证
