Many areas of machine learning and science involve large linear algebra problems, such as
eigendecompositions, solving linear systems, computing matrix exponentials, and trace …

Preconditioning is at the heart of iterative solutions of large, sparse linear systems of
equations in scientific disciplines. Several algebraic approaches, which access no …

In this work, the space-time MORe DWR (M odel O rder Re duction with D ual-W eighted R
esidual error estimates) framework is extended and further developed for single-phase flow …

Subsampling of node sets is useful in contexts such as multilevel methods, computer
graphics, and machine learning. On uniform grid-based node sets, the process of …

Vugular carbonate rocks have a complicated flow behavior because of their multimodal
porosity system, with different interconnectivity at the pore scale. In this study, a new hybrid …

A general circulation ocean model is translated from Fortran to Python. Its code structure is
optimized to exploit available Python utilities, remove simulation bottlenecks, and comply …

We present a novel solver technique for the anisotropic heat flux equation, aimed at the high
level of anisotropy seen in magnetic confinement fusion plasmas. Such problems pose two …

Porous media processes involve various physical phenomena such as mechanical
deformation, transport, and fluid flow. Accurate simulations must capture the strong …

We consider a numerical solution of the mixed dimensional discrete fracture model with
highly conductive fractures. We construct an unstructured mesh that resolves lower …

Spatial heterogeneity (SH), known as the second law of geography, has been a topic of
extensive research. One common approach to analyzing SH involves comparing variances …