67 citations · 137 across the 6 of their papers we have counts for
6 papers
Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data
Jan Niklas Fuhg, Arnav Karmarkar, Teeratorn Kadeethum +2
Parametric surrogate models for partial differential equations (PDEs) are a necessary component for many applications in the computational sciences, and convolutional neural networ…
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
Teeratorn Kadeethum, Francesco Ballarin, Daniel O'Malley +3
We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-…
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties
T. Kadeethum, D. O'Malley, Y. Choi +3
Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In…
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
T. Kadeethum, F. Ballarin, Y. Choi +3
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of sequestration). Here,…
Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Networks
Sung Eun Kim, Hongkyu Yoon, Jonghyun Lee
The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensi…
Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks
Sung Eun Kim, Yongwon Seo, Junshik Hwang +2
Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Co…