A review of surrogate models and their application to groundwater modeling
MJ Asher, BFW Croke, AJ Jakeman… - Water Resources …, 2015 - Wiley Online Library
The spatially and temporally variable parameters and inputs to complex groundwater
models typically result in long runtimes which hinder comprehensive calibration, sensitivity …
models typically result in long runtimes which hinder comprehensive calibration, sensitivity …
Physically based modeling in catchment hydrology at 50: Survey and outlook
C Paniconi, M Putti - Water Resources Research, 2015 - Wiley Online Library
Integrated, process‐based numerical models in hydrology are rapidly evolving, spurred by
novel theories in mathematical physics, advances in computational methods, insights from …
novel theories in mathematical physics, advances in computational methods, insights from …
Optimizing conjunctive use of surface water and groundwater for irrigation to address human-nature water conflicts: A surrogate modeling approach
In arid and semi-arid areas where agriculture competes keenly with ecosystem for water,
integrated management of both surface water (SW) and groundwater (GW) resources at a …
integrated management of both surface water (SW) and groundwater (GW) resources at a …
Reduced-order modeling of subsurface multi-phase flow models using deep residual recurrent neural networks
JN Kani, AH Elsheikh - Transport in Porous Media, 2019 - Springer
We present a reduced-order modeling technique for subsurface multi-phase flow problems
building on the recently introduced deep residual recurrent neural network (DR …
building on the recently introduced deep residual recurrent neural network (DR …
A framework for upscaling and modelling fluid flow for discrete fractures using conditional generative adversarial networks
Scaling up highly heterogeneous aperture distributions of fractures into equivalent
permeability tensors enables a substantial reduction in the computational cost of simulating …
permeability tensors enables a substantial reduction in the computational cost of simulating …
Ensemble Kalman filter versus particle filter for a physically-based coupled surface–subsurface model
The ensemble Kalman filter (EnKF) and sequential importance resampling (SIR) are two
Monte Carlo-based sequential data assimilation (DA) methods developed to solve the …
Monte Carlo-based sequential data assimilation (DA) methods developed to solve the …
Parametric POD-Galerkin model order reduction for unsteady-state heat transfer problems
A parametric reduced order model based on proper orthogonal decomposition with Galerkin
projection has been developed and applied for the modeling of heat transport in T-junction …
projection has been developed and applied for the modeling of heat transport in T-junction …
Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow
The ensemble Kalman filter (EnKF) is a powerful tool for assimilating data in earth system
models. The approach allows real time Bayesian updating of system states and parameters …
models. The approach allows real time Bayesian updating of system states and parameters …
[PDF][PDF] PODMT3DMS-Tool: proper orthogonal decomposition linked to the MT3DMS model for nitrate simulation in aquifers.
Abstract The PODMT3DMS-Tool, which consists of proper orthogonal decomposition (POD)
linked to the Modular Transport 3-Dimensional Multi Species (MT3DMS) code for nitrate …
linked to the Modular Transport 3-Dimensional Multi Species (MT3DMS) code for nitrate …
A reduced‐order model for groundwater flow equation with random hydraulic conductivity: Application to Monte Carlo methods
We present a model‐order reduction technique that overcomes the computational burden
associated with the application of Monte Carlo methods to the solution of the groundwater …
associated with the application of Monte Carlo methods to the solution of the groundwater …