Search Results for author: Shaoxing Mo

Found 4 papers, 3 papers with code

Improving prediction of the terrestrial water storage anomalies during the GRACE and GRACE-FO gap with Bayesian convolutional neural networks

no code implementations21 Jan 2021 Shaoxing Mo, Yulong Zhong, Xiaoqing Shi, Wei Feng, Xin Yin, Jichun Wu

The Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provide valuable and accurate observations of terrestrial water storage anomalies (TWSAs) at a global scale.

Decision Making

Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities

1 code implementation26 Jun 2019 Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu

In addition, a deep residual dense convolutional network (DRDCN) is proposed for surrogate modeling of forward models with high-dimensional and highly-complex mappings.

Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification

1 code implementation22 Dec 2018 Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu

Results indicate that, with relatively limited training data, the deep autoregressive neural network consisting of 27 convolutional layers is capable of providing an accurate approximation for the high-dimensional model input-output relationship.

Computational Efficiency

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

1 code implementation2 Jul 2018 Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu

A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field.

Computational Efficiency regression +1

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