DeepThink IoT: the strength of deep learning in internet of things
Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …
revolutionized technology in the twenty-first century, enabling humans and machines to …
[HTML][HTML] Mapping prospectivity for regolith-hosted REE deposits via convolutional neural network with generative adversarial network augmented data
The regolith-hosted rare earth elements (REE) deposits are the dominant source of the
global heavy REE resources. This study proposed a convolutional neural network (CNN) …
global heavy REE resources. This study proposed a convolutional neural network (CNN) …
An improved tandem neural network architecture for inverse modeling of multicomponent reactive transport in porous media
Parameter estimation for reactive transport models (RTMs) is important in improving their
predictive capacity for accurately simulating subsurface hydrogeochemical processes. This …
predictive capacity for accurately simulating subsurface hydrogeochemical processes. This …
[HTML][HTML] Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology
Essentially all hydrogeological processes are strongly influenced by the subsurface spatial
heterogeneity and the temporal variation of environmental conditions, hydraulic properties …
heterogeneity and the temporal variation of environmental conditions, hydraulic properties …
Variational autoencoder or generative adversarial networks? a comparison of two deep learning methods for flow and transport data assimilation
Groundwater modeling is an important tool for water resources management and aquifer
remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of …
remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of …
Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder
Estimating rock and fluid properties in the subsurface from geophysical measurements is a
computationally and memory-intensive inverse problem. For nonlinear problems with non …
computationally and memory-intensive inverse problem. For nonlinear problems with non …
[HTML][HTML] Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties
Abstract Machine learning-based data-driven modeling can allow computationally efficient
time-dependent solutions of PDEs, such as those that describe subsurface multiphysical …
time-dependent solutions of PDEs, such as those that describe subsurface multiphysical …
Inverse modeling for subsurface flow based on deep learning surrogates and active learning strategies
Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial
to characterize underground geologic properties and reduce prediction uncertainty …
to characterize underground geologic properties and reduce prediction uncertainty …
Characterization of subsurface hydrogeological structures with convolutional conditional neural processes on limited training data
One of the main issues in the application of statistical‐learning‐based methods to the
characterization of hydrological phenomena is the complex parameterization of the high …
characterization of hydrological phenomena is the complex parameterization of the high …
Geophysical inversion using a variational autoencoder to model an assembled spatial prior uncertainty
Prior information regarding subsurface spatial patterns may be used in geophysical
inversion to obtain realistic subsurface models. Field experiments require prior information …
inversion to obtain realistic subsurface models. Field experiments require prior information …