This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning
(EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were …

In this work, we propose an efficient solution of the inverse Stefan problem by multi-fidelity
Bayesian optimization. We construct a multi-fidelity Gaussian process surrogate model by …

We formulate a data-driven, physics-constrained closure method for coarse-scale numerical
simulations of turbulent fluid flows. Our approach involves a closure scheme that is nonlocal …

MFC is an open-source tool for solving multi-component, multi-phase, and bubbly
compressible flows. It is capable of efficiently solving a wide range of flows, including droplet …

Numerical modeling of non-inertial particles dynamics is usually addressed by solving a
population balance equation (PBE). In addition to space and time, a discretization is …

This work presents a data-driven, energy-conserving closure method for the coarse-scale
evolution of the mean and covariance of turbulent systems. Spatiotemporally non-local …

A predictive, point-cloud tracer is presented that determines with a quantified uncertainty the
Lagrangian motion of a group of point-particles within a finite region. The tracer assumes a …

Solving the population balance equation (PBE) for the dynamics of a dispersed phase
coupled to a continuous fluid is expensive. Still, one can reduce the cost by representing the …

The dynamics of cavitation bubbles are important in many flows, but their small sizes and
high number densities often preclude direct numerical simulation. We present a …

Langevin (stochastic differential) equations are routinely used to describe particle-laden
flows. They predict Gaussian probability density functions (PDFs) of a particle's trajectory …