An overview of current applications, challenges, and future trends in distributed process-based models in hydrology
Process-based hydrological models have a long history dating back to the 1960s. Criticized
by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is …
by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is …
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 …
[HTML][HTML] 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 …
Optimization methods for groundwater modeling and management
WWG Yeh - Hydrogeology Journal, 2015 - search.proquest.com
Optimization methods have been used in ground-water modeling as well as for the planning
and management of groundwater systems. This paper reviews and evaluates the various …
and management of groundwater systems. This paper reviews and evaluates the various …
Non-intrusive reduced-order modeling of parameterized electromagnetic scattering problems using cubic spline interpolation
This paper presents a non-intrusive model order reduction (MOR) for the solution of
parameterized electromagnetic scattering problems, which needs to prepare a database …
parameterized electromagnetic scattering problems, which needs to prepare a database …
POD-based model order reduction with an adaptive snapshot selection for a discontinuous Galerkin approximation of the time-domain Maxwell's equations
In this work we report on a reduced-order model (ROM) for the system of time-domain
Maxwell's equations discretized by a discontinuous Galerkin (DG) method. We leverage …
Maxwell's equations discretized by a discontinuous Galerkin (DG) method. We leverage …
Efficient machine-learning surrogates for large-scale geological carbon and energy storage
Geological carbon and energy storage are pivotal for achieving net-zero carbon emissions
and addressing climate change. However, they face uncertainties due to geological factors …
and addressing climate change. However, they face uncertainties due to geological factors …
Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems
Reduced-order modeling is a promising approach, as many phenomena can be described
by a few parameters/mechanisms. An advantage and attractive aspect of a reduced-order …
by a few parameters/mechanisms. An advantage and attractive aspect of a reduced-order …
Parameter-independent model reduction of transient groundwater flow models: Application to inverse problems
A new methodology is proposed for the development of parameter-independent reduced
models for transient groundwater flow models. The model reduction technique is based on …
models for transient groundwater flow models. The model reduction technique is based on …