Jakob Zech
Título
Citado por
Citado por
Año
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
1102018
Exponential ReLU DNN expression of holomorphic maps in high dimension
JAA Opschoor, C Schwab, J Zech
Constructive Approximation, 1-46, 2021
462021
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
362017
Convergence rates of high dimensional Smolyak quadrature
J Zech, C Schwab
ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020
282020
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
272019
Shape holomorphy of the stationary Navier--Stokes equations
A Cohen, C Schwab, J Zech
SIAM Journal on Mathematical Analysis 50 (2), 1720-1752, 2018
272018
Deep neural network expression of posterior expectations in Bayesian PDE inversion
L Herrmann, C Schwab, J Zech
Inverse Problems 36 (12), 125011, 2020
22*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
152020
Sparse-grid approximation of high-dimensional parametric PDEs
J Zech
ETH Zurich, 2018
12*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 on bounded domains
J Zech, Y Marzouk
arXiv preprint arXiv:2006.06994, 2020
82020
Deep learning in high dimension: ReLU network Expression Rates for Bayesian PDE inversion
JAA Opschoor, C Schwab, J Zech
SAM Research Report 2020, OSZ20_920, 2020
42020
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
32019
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
Deep Learning in High Dimension: Neural Network Approximation of Analytic Functions in
C Schwab, J Zech
arXiv preprint arXiv:2111.07080, 2021
1*2021
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
2022
Sparse approximation of triangular transports. Part II: the infinite dimensional case
J Zech, Y Marzouk
arXiv preprint arXiv:2107.13422, 2021
2021
Domain uncertainty quantification in computational electromagnetics
RD Aylwin Pincheira, CF Jerez Hanckes, C Schwab, J Zech
2020
Sparse-Grid Approximation of High-Dimensional Parametric PDEs
J Zech
Dissertation 25683, ETH Zürich, http://dx. doi. org/10.3929/ethz-b-000340651, 2018
2018
High dimensional Smolyak quadrature
J Zech
NumPDE Summer Retreat, Disentis, Switzerland, 2017
2017
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20