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 …
Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications
This survey explores modern approaches for computing low-rank approximations of high-
dimensional matrices by means of the randomized SVD, randomized subspace iteration …
dimensional matrices by means of the randomized SVD, randomized subspace iteration …
Review of machine learning methods applied to enhanced geothermal systems
L Wang, Z Yu, Y Zhang, P Yao - Environmental Earth Sciences, 2023 - Springer
The objective of this study was to summarize the progress in the application of machine
learning (ML) to enhanced geothermal systems (EGSs), including the entire process of EGS …
learning (ML) to enhanced geothermal systems (EGSs), including the entire process of EGS …
A stochastic subspace approach to gradient-free optimization in high dimensions
We present a stochastic descent algorithm for unconstrained optimization that is particularly
efficient when the objective function is slow to evaluate and gradients are not easily …
efficient when the objective function is slow to evaluate and gradients are not easily …
Stochastic subspace descent
We present two stochastic descent algorithms that apply to unconstrained optimization and
are particularly efficient when the objective function is slow to evaluate and gradients are not …
are particularly efficient when the objective function is slow to evaluate and gradients are not …
Randomized algorithms for scientific computing (RASC)
Randomized algorithms have propelled advances in artificial intelligence and represent a
foundational research area in advancing AI for Science. Future advancements in DOE Office …
foundational research area in advancing AI for Science. Future advancements in DOE Office …
Closed-loop field development with multipoint geostatistics and statistical performance assessment
MG Shirangi - Journal of Computational Physics, 2019 - Elsevier
Closed-loop field development (CLFD) optimization is a comprehensive framework for
optimal development of subsurface resources. CLFD involves three major steps: 1) …
optimal development of subsurface resources. CLFD involves three major steps: 1) …
Determination of relative permeability curve under combined effect of polymer and surfactant
W Zhang, J Hou, Y Liu, K Zhou, Z Li, Q Du - Journal of Petroleum Science …, 2022 - Elsevier
Relative permeability of surfactant-polymer (SP) flooding is determined by an inversion
method based on numerical simulation and unsteady-state coreflooding experiment …
method based on numerical simulation and unsteady-state coreflooding experiment …
Pass-efficient randomized algorithms for low-rank matrix approximation using any number of views
EK Bjarkason - SIAM Journal on Scientific Computing, 2019 - SIAM
This paper describes practical randomized algorithms for low-rank matrix approximation that
accommodate any budget for the number of views of the matrix. The presented algorithms …
accommodate any budget for the number of views of the matrix. The presented algorithms …
Modeling unobserved geothermal structures using a physics-informed neural network with transfer learning of prior knowledge
Deep learning has gained attention as a potentially powerful technique for modeling natural-
state geothermal systems; however, its physical validity and prediction inaccuracy at …
state geothermal systems; however, its physical validity and prediction inaccuracy at …