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Jakob Zech
Jakob Zech
Juniorprofessor at Heidelberg University
Bestätigte E-Mail-Adresse bei uni-heidelberg.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
2202018
Exponential ReLU DNN expression of holomorphic maps in high dimension
JAA Opschoor, C Schwab, J Zech
Constructive Approximation 55 (1), 537-582, 2022
1342022
Shape holomorphy of the stationary Navier--Stokes equations
A Cohen, C Schwab, J Zech
SIAM Journal on Mathematical Analysis 50 (2), 1720-1752, 2018
632018
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
622017
Convergence rates of high dimensional Smolyak quadrature
J Zech, C Schwab
ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020
562020
Deep neural network expression of posterior expectations in Bayesian PDE inversion
L Herrmann, C Schwab, J Zech
Inverse Problems 36 (12), 125011, 2020
47*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
442019
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
342023
Sparse-grid approximation of high-dimensional parametric PDEs
J Zech
ETH Zurich, 2018
31*2018
Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case
J Zech, Y Marzouk
Constructive Approximation, 1-68, 2022
30*2022
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
30*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
302020
Neural and spectral operator surrogates: unified construction and expression rate bounds
L Herrmann, C Schwab, J Zech
Advances in Computational Mathematics 50 (4), 72, 2024
25*2024
De Rham compatible deep neural network FEM
M Longo, JAA Opschoor, N Disch, C Schwab, J Zech
Neural Networks 165, 721-739, 2023
24*2023
Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case
J Zech, Y Marzouk
Constructive Approximation 55 (3), 987-1036, 2022
232022
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
222022
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
182015
Distribution learning via neural differential equations: a nonparametric statistical perspective
Y Marzouk, ZR Ren, S Wang, J Zech
Journal of Machine Learning Research 25 (232), 1-61, 2024
152024
Deep operator network approximation rates for Lipschitz operators
C Schwab, A Stein, J Zech
arXiv preprint arXiv:2307.09835, 2023
142023
Mathematical theory of deep learning
P Petersen, J Zech
arXiv preprint arXiv:2407.18384, 2024
92024
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