Teeratorn Kadeethum

According to our database, Teeratorn Kadeethum authored at least 17 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of five.

2024

Harnessing Machine Learning and Data Fusion for Accurate Undocumented Well Identification in Satellite Images.

Teeratorn Kadeethum, Christine Downs

Remote. Sens., June, 2024

2023

Efficient machine-learning surrogates for large-scale geological carbon and energy storage.

Teeratorn Kadeethum, Stephen J. Verzi, Hongkyu Yoon

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

Data-scarce surrogate modeling of shock-induced pore collapse process.

Siu Wun Cheung, Youngsoo Choi, H. Keo Springer, Teeratorn Kadeethum

CoRR, 2023

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

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

Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data.

Jan Niklas Fuhg, Arnav Karmarkar, Teeratorn Kadeethum, Hongkyu Yoon, Nikolaos Bouklas

CoRR, 2022

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

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, Hari S. Viswanathan, Nikolaos Bouklas

Nat. Comput. Sci., 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 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

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

Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation.

Teeratorn Kadeethum, Francesco Ballarin, Nikolaos Bouklas

CoRR, 2021

Physics-informed Neural Networks for Solving Coupled Flow and Transport System.

Sanghyun Lee, Teeratorn Kadeethum

Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

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 Inverse Problems of Nonlinear Biot's Equations: Batch Training.

Teeratorn Kadeethum, Thomas M. Jørgensen, Hamidreza M. Nick

CoRR, 2020

Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations.

Teeratorn Kadeethum, Thomas M. Jørgensen, Hamidreza M. Nick

CoRR, 2020