92 citations · 342 across the 35 of their papers we have counts for
4 papers · 1 filter
A deep learning based reduced order modeling for stochastic underground flow problems
Yiran Wang, Eric Chung, Shubin Fu
In this paper, we propose a deep learning based reduced order modeling method for stochastic underground flow problems in highly heterogeneous media. We aim to utilize supervised l…
An Efficient and Statistically Accurate Lagrangian Data Assimilation Algorithm with Applications to Discrete Element Sea Ice Models
Nan Chen, Shubin Fu, Georgy E Manucharyan
Lagrangian data assimilation of complex nonlinear turbulent flows is an important but computationally challenging topic. In this article, an efficient data-driven statistically acc…
Learning Rays via Deep Neural Network in a Ray-based IPDG Method for High-Frequency Helmholtz Equations in Inhomogeneous Media
Tak Shing Au Yeung, Ka Chun Cheung, Eric T. Chung +2
We develop a deep learning approach to extract ray directions at discrete locations by analyzing highly oscillatory wave fields. A deep neural network is trained on a set of local…
A local-global generalized multiscale finite element method for highly heterogeneous stochastic groundwater flow problems
Yiran Wang, Eric Chung, Shubin Fu
In this paper, we propose a local-global multiscale method for highly heterogeneous stochastic groundwater flow problems under the framework of reduced basis method and the general…