Zobrazeno 1 - 10
of 57
pro vyhledávání: '"KADEETHUM, TEERATORN"'
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 and operational limitations, resulting in possibilities of induced seis
Externí odkaz:
http://arxiv.org/abs/2310.07461
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 information. To address this challenge, we propose a progressive reduced or
Externí odkaz:
http://arxiv.org/abs/2310.03770
Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore col
Externí odkaz:
http://arxiv.org/abs/2306.00184
Parametric surrogate models for partial differential equations (PDEs) are a necessary component for many applications in the computational sciences, and convolutional neural networks (CNNs) have proved as an excellent tool to generate these surrogate
Externí odkaz:
http://arxiv.org/abs/2206.04675
Autor:
Kadeethum, Teeratorn, Ballarin, Francesco, O'Malley, Daniel, Choi, Youngsoo, Bouklas, Nikolaos, Yoon, Hongkyu
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-convolutional autoencoders (DC-AE) has been shown to capture nonlinear
Externí odkaz:
http://arxiv.org/abs/2202.05460
Autor:
SANGHYUN LEE1 lee@math.fsu.edu, KADEETHUM, TEERATORN2 tkadeet@sandia.gov, NICK, HAMIDREZA M.3 hamid@dtu.dk
Publikováno v:
International Journal of Numerical Analysis & Modeling. 2024, Vol. 21 Issue 5, p764-792. 29p.
Autor:
Kadeethum, Teeratorn, O'Malley, Daniel, Fuhg, Jan Niklas, Choi, Youngsoo, Lee, Jonghyun, Viswanathan, Hari S., Bouklas, Nikolaos
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 inverse solution operator of partial differential equations (PDEs). Even t
Externí odkaz:
http://arxiv.org/abs/2105.13136
Autor:
Kadeethum, Teeratorn1 (AUTHOR), O’Malley, Daniel2 (AUTHOR), Choi, Youngsoo3 (AUTHOR), Viswanathan, Hari S.2 (AUTHOR), Yoon, Hongkyu1 (AUTHOR) hyoon@sandia.gov
Publikováno v:
Scientific Reports. 7/8/2024, Vol. 14 Issue 1, p1-13. 13p.
Autor:
Kadeethum, Teeratorn1 (AUTHOR), Downs, Christine1 (AUTHOR) cdowns@sandia.gov
Publikováno v:
Remote Sensing. Jun2024, Vol. 16 Issue 12, p2116. 20p.
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 studied by utilizing the neural network and machine learning. It is cru
Externí odkaz:
http://arxiv.org/abs/2007.10119