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Joost A. A. Opschoor
Joost A. A. Opschoor
ETH Zürich, Seminar for Applied Mathematics
Verified email at sam.math.ethz.ch
Title
Cited by
Cited by
Year
Deep ReLU networks and high-order finite element methods
JAA Opschoor, PC Petersen, C Schwab
Analysis and Applications 18 (05), 715-770, 2020
962020
Exponential ReLU DNN expression of holomorphic maps in high dimension
JAA Opschoor, C Schwab, J Zech
Constructive Approximation 55 (1), 537-582, 2022
662022
Exponential relu neural network approximation rates for point and edge singularities
C Marcati, JAA Opschoor, PC Petersen, C Schwab
Foundations of Computational Mathematics, 1-85, 2022
72022
Constructive deep ReLU neural network approximation
L Herrmann, JAA Opschoor, C Schwab
Journal of Scientific Computing 90 (2), 1-37, 2022
72022
Deep learning in high dimension: ReLU network expression rates for bayesian PDE inversion
JAA Opschoor, C Schwab, J Zech
SAM Research Report 2020, 2020
72020
De Rham compatible Deep Neural Networks
M Longo, JAA Opschoor, N Disch, C Schwab, J Zech
arXiv preprint arXiv:2201.05395, 2022
22022
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
2022
ReLU DNN expression of sparse gpc expansion in uncertainty quantification
JAA Opschoor, C Schwab
Exponential Deep Neural Network Expression for Solution Sets of PDEs Christoph Schwab Seminar for Applied Mathematics ETH Zürich, Switzerland
J Opschoor, CM ETH, L Gonon, L Herrmann, P Petersen, J Zech
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