Core Concepts
Proposing a learning-based multi-continuum model to enhance the accuracy of solutions for multiscale flow problems.
Abstract
The content introduces a novel learning-based multi-continuum model to improve the accuracy of solutions for multiscale flow problems. It discusses the challenges in numerical homogenization and presents a method that enriches the homogenized equation using deep learning techniques. The article outlines the structure of the proposed model, including two continua with neural network parameterizations for permeability and transfer coefficients. It explains the forward solver methods, such as FEM and PINN, for linear and nonlinear equations, respectively. The optimization process involves gradient descent and adjoint methods to optimize network parameters efficiently.
Stats
"Multiscale problems can usually be approximated through numerical homogenization by an equation with some effective parameters that can capture the macroscopic behavior of the original system on the coarse grid to speed up the simulation."
"Our proposed learning-based multi-continuum model can resolve multiple interacted media within each coarse grid block and describe the mass transfer among them."
Quotes
"Our proposed learning-based multi-continuum model can resolve multiple interacted media within each coarse grid block."
"Multiscale problems can usually be approximated through numerical homogenization by an equation with some effective parameters."