Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. - Shaoxing Mo; Yinhao Zhu; Nicholas Zabaras; Xiaoqing Shi; Jichun Wu (CoRR, 2018)

Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification. - Shaoxing Mo; Nicholas Zabaras; Xiaoqing Shi; Jichun Wu (CoRR, 2018)

Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields. - Shaoxing Mo; Nicholas Zabaras; Xiaoqing Shi; Jichun Wu (CoRR, 2019)

An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling. - Shaoxing Mo; Xiaoqing Shi; Dan Lu 001; Ming Ye; Jichun Wu (Comput. Geosci., 2019)

Filling the gap between GRACE- and GRACE-FO-derived terrestrial water storage anomalies with Bayesian convolutional neural networks. - Shaoxing Mo; Yulong Zhong; Xiaoqing Shi; Wei Feng 006; Xin Yin; Jichun Wu (CoRR, 2021)