Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification-Reference-Cited by-同舟云学术

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

Published:2018-12 Issue: Volume:375 Page:565-588
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Tripathy Rohit K.,Bilionis Ilias

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modelling and Simulation,Numerical Analysis

Reference75 articles.

1. Uncertainty Quantification: Theory, Implementation, and Applications, vol. 12;Smith,2013

2. Introduction to Uncertainty Quantification, vol. 63;Sullivan,2015

3. Monte Carlo Strategies in Scientific Computing;Liu,2008

4. Monte Carlo Simulation, vol. 116;Mooney,1997

5. Monte Carlo Methods;Robert,2004

Cited by 301 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. Learning experimental data to predict fluid-elastic instability and optimize configuration of tube arrays;Annals of Nuclear Energy;2024-12

2. POD–ANN as digital twins for surge line thermal stratification;Nuclear Engineering and Design;2024-11

3. A bi-fidelity DeepONet approach for modeling hysteretic systems under uncertainty;Applied Mathematical Modelling;2024-11

4. An offline data-driven dual-surrogate framework considering prediction error for history matching;Computers & Geosciences;2024-10

5. Predicting the seismic performance of large-scale dome structures with hybrid uncertainties based on Bayesian inference;Engineering Applications of Artificial Intelligence;2024-10