121 citations · 123 across the 4 of their papers we have counts for
5 papers
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows
Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya +1
Deep Learning (DL) algorithms are becoming increasingly popular for the reconstruction of high-resolution turbulent flows (aka super-resolution). However, current DL approaches per…
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets
Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya +1
Deep Learning (DL) algorithms are emerging as a key alternative to computationally expensive CFD simulations. However, state-of-the-art DL approaches require large and high-resolut…
Automating Artifact Detection in Video Games
Parmida Davarmanesh, Kuanhao Jiang, Tingting Ou +5
In spite of advances in gaming hardware and software, gameplay is often tainted with graphics errors, glitches, and screen artifacts. This proof of concept study presents a machine…
CFDNet: a deep learning-based accelerator for fluid simulations
Octavi Obiols-Sales, Abhinav Vishnu, Nicholas Malaya +1
CFD is widely used in physical system design and optimization, where it is used to predict engineering quantities of interest, such as the lift on a plane wing or the drag on a mot…
The Parallel C++ Statistical Library for Bayesian Inference: QUESO
Damon McDougall, Nicholas Malaya, Robert D. Moser
The Parallel C++ Statistical Library for the Quantification of Uncertainty for Estimation, Simulation and Optimization, Queso, is a collection of statistical algorithms and program…