activity
20162022
most citedUsing the pyMIC Offload Module in PyFR

2 citations · 4 across the 7 of their papers we have counts for

collaborators

14 papers

math.NA2022

Nonlinear p-multigrid preconditioner for implicit time integration of compressible Navier--Stokes equations

Lai Wang, Will Trojak, Freddie Witherden +1

Within the framework of -adaptive flux reconstruction, we aim to construct efficient polynomial multigrid (MG) preconditioners for implicit time integration of the Navier--…

math.NA2021

Enabling four-dimensional conformal hybrid meshing with cubic pyramids

Miroslav S. Petrov, Todor D. Todorov, Gage S. Walters +2

The main purpose of this article is to develop a novel refinement strategy for four-dimensional hybrid meshes based on cubic pyramids. This optimal refinement strategy subdivides a…

math.NA2020

Foundations of space-time finite element methods: polytopes, interpolation, and integration

Cory V. Frontin, Gage S. Walters, Freddie D. Witherden +3

The main purpose of this article is to facilitate the implementation of space-time finite element methods in four-dimensional space. In order to develop a finite element method in…

math.NA2020

A Riemann Difference Scheme for Shock Capturing in Discontinuous Finite Element Methods

Tarik Dzanic, Will Trojak, Freddie D. Witherden

We present a novel structure-preserving numerical scheme for discontinuous finite element approximations of nonlinear hyperbolic systems. The method can be understood as a generali…

math.NA20201 cited

On Fourier analysis of polynomial multigrid for arbitrary multi-stage cycles

Will Trojak, Freddie D. Witherden

The Fourier analysis of the \emph{p}-multigrid acceleration technique is considered for a dual-time scheme applied to the advection-diffusion equation with various cycle configurat…

physics.flu-dyn2020

Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations

Salar Taghizadeh, Freddie D. Witherden, Sharath S. Girimaji

A recent thrust in turbulence closure modeling research is to incorporate machine learning (ML) elements, such as neural networks, for the purpose of enhancing the predictive capab…