A machine learning approach for efficient uncertainty quantification using multiscale methods

12 November 2017

Papers citing "A machine learning approach for efficient uncertainty quantification using multiscale methods"

9 / 9 papers shown

Title
Adaptive and Parallel Multiscale Framework for Modeling Cohesive Failure in Engineering Scale Systems Sion Kim Ezra Kissel Karel Matous AI4CE 32 1 0 18 Apr 2024
Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model Jun Yu Li Dongxiao Zhang Tianhao He Q. Zheng AI4CE 27 6 0 28 May 2022
Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based Metamodels S. Mohammadzadeh Emma Lejeune AI4CE 28 28 0 24 Jul 2021
On generalized residue network for deep learning of unknown dynamical systems Zhen Chen D. Xiu AI4CE 19 46 0 23 Jan 2020
Data-Driven Deep Learning of Partial Differential Equations in Modal Space Kailiang Wu D. Xiu 20 149 0 15 Oct 2019
Data Driven Governing Equations Approximation Using Deep Neural Networks Tong Qin Kailiang Wu D. Xiu PINN 34 270 0 13 Nov 2018
Data-driven polynomial chaos expansion for machine learning regression Emiliano Torre S. Marelli P. Embrechts Bruno Sudret 31 130 0 09 Aug 2018
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media S. Mo Yinhao Zhu N. Zabaras Xiaoqing Shi Jichun Wu AI4CE 16 272 0 02 Jul 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015