Artículos con órdenes de acceso público - Jakob ZechMás información
Disponibles en algún lugar: 16
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
Órdenes: Swiss National Science Foundation
Exponential ReLU DNN expression of holomorphic maps in high dimension
JAA Opschoor, C Schwab, J Zech
Constructive Approximation 55 (1), 537-582, 2022
Órdenes: Swiss National Science Foundation
Shape holomorphy of the stationary Navier--Stokes equations
A Cohen, C Schwab, J Zech
SIAM Journal on Mathematical Analysis 50 (2), 1720-1752, 2018
Órdenes: Swiss National Science Foundation, European Commission
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
Órdenes: Swiss National Science Foundation
Convergence rates of high dimensional Smolyak quadrature
J Zech, C Schwab
ESAIM: Mathematical Modelling and Numerical Analysis 54 (4), 1259-1307, 2020
Órdenes: Swiss National Science Foundation
Deep neural network expression of posterior expectations in Bayesian PDE inversion
L Herrmann, C Schwab, J Zech
Inverse Problems 36 (12), 125011, 2020
Órdenes: Swiss National Science Foundation
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
Órdenes: Swiss National Science Foundation
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
Órdenes: UK Engineering and Physical Sciences Research Council
Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case
J Zech, Y Marzouk
Constructive Approximation, 1-68, 2022
Órdenes: US Department of Energy, Swiss National Science Foundation
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
Órdenes: Swiss National Science Foundation
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
Órdenes: Swiss National Science Foundation
Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case
J Zech, Y Marzouk
Constructive Approximation 55 (3), 987-1036, 2022
Órdenes: US Department of Energy, Swiss National Science Foundation
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
Órdenes: US National Science Foundation, US Department of Energy, US Department of …
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
Órdenes: Swiss National Science Foundation
Multilevel domain uncertainty quantification in computational electromagnetics
R Aylwin, C Jerez-Hanckes, C Schwab, J Zech
Mathematical Models and Methods in Applied Sciences 33 (04), 877-921, 2023
Órdenes: Swiss National Science Foundation
Deep learning in high dimension: ReLU neural network expression for Bayesian PDE inversion (extended version)
JAA Opschoor, C Schwab, J Zech
Órdenes: Swiss National Science Foundation
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