Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification-Reference-Cited by-同舟云学术

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Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification

Published:2022-08-23 Issue:3 Volume:10 Page:975-1011
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Scarabosio Laura¹^ORCID

Affiliation:

1. Radboud University, Institute for Mathematics, Astrophysics and Particle Physics, Nijmegen, 6512 JB, The Netherlands ().

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference70 articles.

1. The Gap between Theory and Practice in Function Approximation with Deep Neural Networks

2. Data-driven discovery of PDEs in complex datasets

3. Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations

4. Regularity of the Maxwell equations in heterogeneous media and Lipschitz domains

5. Convergence analysis of Padé approximations for Helmholtz frequency response problems

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Shape Uncertainty Quantification for Electromagnetic Wave Scattering via First-Order Sparse Boundary Element Approximation;IEEE Transactions on Antennas and Propagation;2024-08

2. An operator learning perspective on parameter-to-observable maps;Foundations of Data Science;2024

3. Learning smooth functions in high dimensions;Handbook of Numerical Analysis;2024

4. Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems;Neurocomputing;2023-12

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