In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …

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 …

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 …

We introduce a hyperbolic closure for the Grad moment expansion of the Bhatnagar-Gross-
Krook's (BGK) kinetic model using a neural network (NN) trained on BGK's moment data …

This paper assesses and reports the experience of ten teams working to port, validate, and
benchmark several High Performance Computing applications on a novel GPU-accelerated …

Modeling and analysis of turbulent fluid flows remains one of the challenging areas of fluid
mechanics where integration of the full equations is associated with extreme computational …