85 citations · 176 across the 4 of their papers we have counts for
4 papers
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer
Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi +2
Data-driven modeling can suffer from a constant demand for data, leading to reduced accuracy and impractical for engineering applications due to the high cost and scarcity of infor…
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,…
Modeling flow and transport in fracture networks using graphs
S. Karra, D. O'Malley, J. D. Hyman +2
Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital fo…