44 citations · 174 across the 13 of their papers we have counts for
12 papers
Accelerating multigrid solver with generative super-resolution
Francisco Holguin, GS Sidharth, Gavin Portwood
The geometric multigrid algorithm is an efficient numerical method for solving a variety of elliptic partial differential equations (PDEs). The method damps errors at progressively…
Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows
Sam F. Lewin, Stephen M. de Bruyn Kops, Gavin D. Portwood +1
Motivated by oceanographic observational datasets, we propose a probabilistic neural network (PNN) model for calculating turbulent energy dissipation rates from vertical columns of…
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow
Varun Shankar, Gavin D. Portwood, Arvind T. Mohan +6
In fluid physics, data-driven models to enhance or accelerate solution methods are becoming increasingly popular for many application domains, such as alternatives to turbulence cl…
Analysis of scale-dependent kinetic and potential energy in sheared, stably stratified turbulence
Xiaolong Zhang, Rohit Dhariwal, Gavin Portwood +2
Budgets of turbulent kinetic energy (TKE) and turbulent potential energy (TPE) at different scales in sheared, stably stratified turbulence are analyzed using a filtering ap…
Multigrid Solver With Super-Resolved Interpolation
Francisco Holguin, GS Sidharth, Gavin Portwood
The multigrid algorithm is an efficient numerical method for solving a variety of elliptic partial differential equations (PDEs). The method damps errors at progressively finer gri…
Idealised Turbulent Wake With Steady, Non-Uniform Ambient Density Stratification
G. D. Portwood, S. M. de Bruyn Kops
Density stratification in geophysical environments can be non-uniform, particularly in thermohaline staircases and atmospheric layer transitions. Non-uniform stratification, howeve…
Implications of inertial subrange scaling for stably stratified mixing
G. D. Portwood, S. M. de Bruyn Kops, C. P. Caulfield
The effects of turbulent dynamic range on scalar mixing in stably stratified turbulence are investigated by an adaptation of the theoretical passive scalar modelling arguments of B…
Accelerating Training in Artificial Neural Networks with Dynamic Mode Decomposition
Mauricio E. Tano, Gavin D. Portwood, Jean C. Ragusa
Training of deep neural networks (DNNs) frequently involves optimizing several millions or even billions of parameters. Even with modern computing architectures, the computational…
Interpreting neural network models of residual scalar flux
Gavin D. Portwood, Balasubramanya T. Nadiga, Juan A. Saenz +1
We show that in addition to providing effective and competitive closures, when analysed in terms of dynamics and physically-relevant diagnostics, artificial neural networks (ANNs)…
Turbulence forecasting via Neural ODE
Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro +9
Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that ar…
Unsupervised Machine Learning to Teach Fluid Dynamicists to Think in 15 Dimensions
S. M. de Bruyn Kops, D. J. Saunders, E. A. Rietman +1
An autoencoder is used to compress and then reconstruct three-dimensional stratified turbulence data in order to better understand fluid dynamics by studying the errors in the reco…
Asymptotic dynamics of high dynamic range stratified turbulence
G. D. Portwood, S. M. de Bruyn Kops, C. P. Caulfield
Direct numerical simulations of homogeneous sheared and stably stratified turbulence are considered to probe the asymptotic high-dynamic range regime suggested by Gargett et al. 19…