24 citations · 57 across the 8 of their papers we have counts for
8 papers
Learning Physics from an Imperfect Ancestor
S. Mohammad Mousavi, Teeratorn Kadeethum, Nikolaos Bouklas +1
Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependen…
Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods
Mohammad Nooraiepour, Jakub Wiktor Both, Teeratorn Kadeethum +1
Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges…
Efficient machine-learning surrogates for large-scale geological carbon and energy storage
Teeratorn Kadeethum, Stephen J. Verzi, Hongkyu Yoon
Geological carbon and energy storage are pivotal for achieving net-zero carbon emissions and addressing climate change. However, they face uncertainties due to geological factors a…
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…
Data-scarce surrogate modeling of shock-induced pore collapse process
Siu Wun Cheung, Youngsoo Choi, H. Keo Springer +1
Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological science…
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…