Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Surrogate strategies are used widely for uncertainty quantification of groundwater models in
order to improve computational efficiency. However, their application to dynamic multiphase …
order to improve computational efficiency. However, their application to dynamic multiphase …
Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification
Identification of a groundwater contaminant source simultaneously with the hydraulic
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …
An analysis platform for multiscale hydrogeologic modeling with emphasis on hybrid multiscale methods
One of the most significant challenges faced by hydrogeologic modelers is the disparity
between the spatial and temporal scales at which fundamental flow, transport, and reaction …
between the spatial and temporal scales at which fundamental flow, transport, and reaction …
Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis
G Blatman - 2009 - inis.iaea.org
Mathematical models are widely used in many science disciplines, such as physics, biology
and meteorology. They are aimed at better understanding and explaining real-world …
and meteorology. They are aimed at better understanding and explaining real-world …
Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non‐Gaussian hydraulic conductivities
Inverse modeling for the estimation of non‐Gaussian hydraulic conductivity fields in
subsurface flow and solute transport models remains a challenging problem. This is mainly …
subsurface flow and solute transport models remains a challenging problem. This is mainly …
Efficient B ayesian experimental design for contaminant source identification
In this study, an efficient full Bayesian approach is developed for the optimal sampling well
location design and source parameters identification of groundwater contaminants. An …
location design and source parameters identification of groundwater contaminants. An …
Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence
In this work, we propose a new Gaussian process regression (GPR)-based multifidelity
method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the …
method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the …
Kernel principal component analysis for stochastic input model generation
Stochastic analysis of random heterogeneous media provides useful information only if
realistic input models of the material property variations are used. These input models are …
realistic input models of the material property variations are used. These input models are …
A sparse grid based Bayesian method for contaminant source identification
In this study, an efficient Bayesian method based on the adaptive sparse grid interpolation is
used to solve the contaminant source identification problem. The unknown parameters that …
used to solve the contaminant source identification problem. The unknown parameters that …
A meshfree peridynamic model for brittle fracture in randomly heterogeneous materials
In this work we aim to develop a unified mathematical framework and a reliable
computational approach to model the brittle fracture in heterogeneous materials with …
computational approach to model the brittle fracture in heterogeneous materials with …