133 citations · 215 across the 9 of their papers we have counts for
9 papers
Enhancing neural network extrapolation in thermo-fluid systems using steady-state solutions
Sanjeeb Poudel, Teeratorn Kadeethum, Sanghyun Lee
Time-dependent partial differential equations (PDEs) arise in many engineering systems, including thermo-fluid applications. Classical numerical simulations of such systems can bec…
Reference-Space Reuse for Steady and Transient State-Constrained Conjugate Heat Transfer
Teeratorn Kadeethum, Francesco Ballarin, Youngsoo Choi +1
Repeated thermal optimization must enforce local temperature limits while solving linear systems whose unknowns change as the constraints activate and release. Zero-extension trans…
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg +4
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inve…
Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
T. Kadeethum, F. Ballarin, N. Bouklas
We present a non-intrusive model reduction framework for linear poroelasticity problems in heterogeneous porous media using proper orthogonal decomposition (POD) and neural network…
Enriched Galerkin Discretization for Modeling Poroelasticity and Permeability Alteration in Heterogeneous Porous Media
T. Kadeethum, H. M. Nick, S. Lee +1
Accurate simulation of the coupled fluid flow and solid deformation in porous media is challenging, especially when the media permeability and storativity are heterogeneous. We app…
A Locally Conservative Mixed Finite Element Framework for Coupled Hydro-Mechanical-Chemical Processes in Heterogeneous Porous Media
T. Kadeethum, S. Lee, F. Ballarin +2
This paper presents a mixed finite element framework for coupled hydro-mechanical-chemical processes in heterogeneous porous media. The framework combines two types of locally cons…
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
In this paper, the optimal choice of the interior penalty parameter of the discontinuous Galerkin finite element methods for both the elliptic problems and the Biot's systems are s…
Physics-informed Neural Networks for Solving Inverse Problems of Nonlinear Biot's Equations: Batch Training
Teeratorn Kadeethum, Thomas M Jørgensen, Hamidreza M Nick
In biomedical engineering, earthquake prediction, and underground energy harvesting, it is crucial to indirectly estimate the physical properties of porous media since the direct m…
Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations
Teeratorn Kadeethum, Thomas M Jorgensen, Hamidreza M Nick
This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical en…