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
1322018
Exponential ReLU DNN expression of holomorphic maps in high dimension
JAA Opschoor, C Schwab, J Zech
Constructive Approximation 55 (1), 537-582, 2022
652022
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
402017
Convergence rates of high dimensional Smolyak quadrature
J Zech, C Schwab
ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020
342020
Shape holomorphy of the stationary Navier--Stokes equations
A Cohen, C Schwab, J Zech
SIAM Journal on Mathematical Analysis 50 (2), 1720-1752, 2018
332018
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
302019
Deep neural network expression of posterior expectations in Bayesian PDE inversion
L Herrmann, C Schwab, J Zech
Inverse Problems 36 (12), 125011, 2020
27*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
162020
Sparse-grid approximation of high-dimensional parametric PDEs
J Zech
ETH Zurich, 2018
13*2018
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
112015
Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case
J Zech, Y Marzouk
Constructive Approximation, 1-68, 2022
10*2022
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
7*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
42019
Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case
J Zech, Y Marzouk
Constructive Approximation 55 (3), 987-1036, 2022
22022
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
22014
De Rham compatible Deep Neural Networks
M Longo, JAA Opschoor, N Disch, C Schwab, J Zech
arXiv preprint arXiv:2201.05395, 2022
12022
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
12022
Neural and gpc operator surrogates: construction and expression rate bounds
L Herrmann, C Schwab, J Zech
arXiv preprint arXiv:2207.04950, 2022
2022
Multilevel Optimization for Inverse Problems
S Weissmann, A Wilson, J Zech
arXiv preprint arXiv:2204.13732, 2022
2022
The system can't perform the operation now. Try again later.
Articles 1–20