Fast and scalable earth texture synthesis using spatially assembled generative adversarial neural networks. Kim SE, Lee J, Yoon H. SE Kim, H Yoon, J Lee - Journal of Contaminant Hydrology, 2021 - Elsevier GSID: Xt99_KcMNNwJ
Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation. Chen Q, Cui Z, Ma X. Q Chen, Z Cui, G Liu, Z Yang, X Ma - Journal of Hydrology, 2022 - Elsevier GSID: y9uwcUDaGDUJ
Fast and scalable earth texture synthesis using spatially assembled generative adversarial neural networks. Kim SE, Lee J, Yoon H. SE Kim, H Yoon, J Lee - Journal of Contaminant Hydrology, 2021 - Elsevier GSID: Xt99_KcMNNwJ
Applications of physics-informed scientific machine learning in subsurface science: A survey. Yoon H. AY Sun, H Yoon, CY Shih, Z Zhong - arXiv preprint arXiv:2104.04764, 2021 - arxiv.org GSID: UrvxCNFarg0J
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. Choi Y, Kadeethum T, O'Malley D. T Kadeethum, D O'Malley, Y Choi… - arXiv preprint arXiv …, 2021 - arxiv.org GSID: 2zc5UQqlWB4J
