An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

By Guannan Zhang, Dan Lu, Ming Ye, Max D. Gunzburger, Clayton G. Webster. 2013

97 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fbc7cd91795a5ea56c0441b49e4810c34ce4d661

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Ming Ye
2. Nicholas Zabaras
3. Yinhao Zhu

In Environmental Engineering

1. Ming Ye
2. H. Dai
3. Dan Lu
4. P. Meyer
5. A. Elshall
6. Peigui Liu
7. Gary P. Curtis
8. Yuezan Tao

In Statistics and Probability

1. Nicholas Zabaras
2. Yinhao Zhu
3. Ming Ye
4. H. Dai
5. Jichun Wu
6. Xiaoqing Shi
7. Shaoxing Mo
8. Peigui Liu

Citations in the corpus

• Groundwater sustainability: a review of the interactions between science and policy (2020, methodology): “Other alternatives include using parallel computing (e.g. Elshall et al 2015) or surrogate models (e.g. Zhang et al 2013).”
• Assessment of parametric uncertainty for groundwater reactive transport modeling (2014, methodology): “, 2013] and sparse grid collocation [Zhang et al., 2013] within the DREAM framework.”
• Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media (2018, background): “…cost, in which the training samples for surrogate construction are adaptively selected, e.g., based on the importance of dimensions [ Ganapathysubramanian and Zabaras , 2007; Liao et al. , 2017] or local response nonlinearity [ Ma and Zabaras , 2009; Mo et al. , 2017; Zhang et al. , 2013].”
• Evaluating marginal likelihood with thermodynamic integration method and comparison with several other numerical methods (2016, methodology): “This is feasible, given that surrogate modeling has been widely used in uncertainty quantification of groundwater modeling [Razavi et al., 2012; Laloy et al., 2013; Zhang et al., 2013].”
• Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, methodology): “For example, Zhang et al. (2013a, 2013b) used optimization to identify high-probability regions before SGC surrogate was built, and adaptive SGC was used for the high probability regions to save computational cost.”