Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations. - Teeratorn Kadeethum; Thomas M. Jørgensen; Hamidreza M. Nick (CoRR, 2020)

Physics-informed Neural Networks for Solving Inverse Problems of Nonlinear Biot's Equations: Batch Training. - Teeratorn Kadeethum; Thomas M. Jørgensen; Hamidreza M. Nick (CoRR, 2020)

Choice of Interior Penalty Coefficient for Interior Penalty Discontinuous Galerkin Method for Biot's System by Employing Machine Learning. - Sanghyun Lee; Teeratorn Kadeethum; Hamidreza M. Nick (CoRR, 2020)

Physics-informed Neural Networks for Solving Coupled Flow and Transport System. - Sanghyun Lee; Teeratorn Kadeethum (AAAI Spring Symposium: MLPS, 2021)

Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation. - Teeratorn Kadeethum; Francesco Ballarin; Nikolaos Bouklas (CoRR, 2021)

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. - Teeratorn Kadeethum; Francesco Ballarin; Youngsoo Choi; Daniel O'Malley; Hongkyu Yoon; Nikolaos Bouklas (CoRR, 2021)

A locally conservative mixed finite element framework for coupled hydro-mechanical-chemical processes in heterogeneous porous media. - Teeratorn Kadeethum; Sanghyun Lee; Francesco Ballarin; Jinhyun Choo; Hamidreza M. Nick (Comput. Geosci., 2021)

Enriched Galerkin discretization for modeling poroelasticity and permeability alteration in heterogeneous porous media. - Teeratorn Kadeethum; Hamidreza M. Nick; Sanghyun Lee; Francesco Ballarin (J. Comput. Phys., 2021)

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. - Teeratorn Kadeethum; Daniel O'Malley; Jan Niklas Fuhg; Youngsoo Choi; Jonghyun Lee 005; Hari S. Viswanathan; Nikolaos Bouklas (Nat. Comput. Sci., 2021)

Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. - Teeratorn Kadeethum; Francesco Ballarin; Daniel O'Malley; Youngsoo Choi; Nikolaos Bouklas; Hongkyu Yoon (CoRR, 2022)

Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data. - Jan Niklas Fuhg; Arnav Karmarkar; Teeratorn Kadeethum; Hongkyu Yoon; Nikolaos Bouklas (CoRR, 2022)

Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. - Teeratorn Kadeethum; Daniel O'Malley; Youngsoo Choi; Hari S. Viswanathan; Nikolaos Bouklas; Hongkyu Yoon (Comput. Geosci., 2022)

Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. - Teeratorn Kadeethum; John D. Jakeman; Youngsoo Choi; Nikolaos Bouklas; Hongkyu Yoon (IEEE Access, 2023)

Data-scarce surrogate modeling of shock-induced pore collapse process. - Siu Wun Cheung; Youngsoo Choi; H. Keo Springer; Teeratorn Kadeethum (CoRR, 2023)

Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer. - Teeratorn Kadeethum; Daniel O'Malley; Youngsoo Choi; Hari S. Viswanathan; Hongkyu Yoon (CoRR, 2023)

Efficient machine-learning surrogates for large-scale geological carbon and energy storage. - Teeratorn Kadeethum; Stephen J. Verzi; Hongkyu Yoon (CoRR, 2023)

Harnessing Machine Learning and Data Fusion for Accurate Undocumented Well Identification in Satellite Images. - Teeratorn Kadeethum; Christine Downs (Remote. Sens., 2024)