Follow
Jakob Zech
Title
Cited by
Cited by
Year
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
1902018
Exponential ReLU DNN expression of holomorphic maps in high dimension
JAA Opschoor, C Schwab, J Zech
Constructive Approximation 55 (1), 537-582, 2022
1052022
Shape holomorphy of the stationary Navier--Stokes equations
A Cohen, C Schwab, J Zech
SIAM Journal on Mathematical Analysis 50 (2), 1720-1752, 2018
532018
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
532017
Convergence rates of high dimensional Smolyak quadrature
J Zech, C Schwab
ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020
482020
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
402019
Deep neural network expression of posterior expectations in Bayesian PDE inversion
L Herrmann, C Schwab, J Zech
Inverse Problems 36 (12), 125011, 2020
39*2020
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
212020
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
172015
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
16*2023
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
162022
Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case
J Zech, Y Marzouk
Constructive Approximation 55 (3), 987-1036, 2022
152022
De Rham compatible deep neural network FEM
M Longo, JAA Opschoor, N Disch, C Schwab, J Zech
Neural Networks 165, 721-739, 2023
132023
Neural and gpc operator surrogates: construction and expression rate bounds
L Herrmann, C Schwab, J Zech
arXiv preprint arXiv:2207.04950, 2022
92022
Distribution learning via neural differential equations: a nonparametric statistical perspective
Y Marzouk, Z Ren, S Wang, J Zech
arXiv preprint arXiv:2309.01043, 2023
52023
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
52019
Deep operator network approximation rates for Lipschitz operators
C Schwab, A Stein, J Zech
arXiv preprint arXiv:2307.09835, 2023
32023
The system can't perform the operation now. Try again later.
Articles 1–20