In terms of multiple temporal and spatial scales, massive data from experiments, flow field
measurements, and high-fidelity numerical simulations have greatly promoted the rapid …

Reduced-order modeling (ROM) of fluid flows has been an active area of research for
several decades. The huge computational cost of direct numerical simulations has motivated …

For the nonlinear Richards equation as an unsaturated flow through heterogeneous media,
we build a new coarse-scale approximation algorithm utilizing numerical homogenization …

In this work, we present a novel nonlocal nonlinear coarse grid approximation using a
machine learning algorithm. We consider unsaturated and two-phase flow problems in …

History matching can play a key role in improving geological characterization and reducing
the uncertainty of reservoir model predictions. Application of reservoir history matching is …

Pore-scale modeling has limited applicability at large scales due to its high computational
cost. One common approach to upscale pore-scale models is the use of effective medium …

In this work, we propose a multi-stage training strategy for the development of deep learning
algorithms applied to problems with multiscale features. Each stage of the proposed strategy …

In this work, we propose a multi-agent actor-critic reinforcement learning (RL) algorithm to
accelerate the multi-level Monte Carlo Markov Chain (MCMC) sampling algorithms. The …

This letter is concerned with real-time background flow field estimation using distributed
pressure sensor measurements for autonomous underwater vehicles (AUVs). The goal of …

Multigrid preconditioners are one of the most powerful techniques for solving large sparse
linear systems. In this research, we address Darcy flow problems with random permeability …