129 citations · 260 across the 9 of their papers we have counts for
9 papers
Experiences Readying Applications for Exascale
Paul T. Bauman, Reuben D. Budiardja, Dmytro Bykov +29
The advent of exascale computing invites an assessment of existing best practices for developing application readiness on the world's largest supercomputers. This work details obse…
An Estimator for the Sensitivity to Perturbations of Deep Neural Networks
Naman Maheshwari, Nicholas Malaya, Scott Moe +2
For Deep Neural Networks (DNNs) to become useful in safety-critical applications, such as self-driving cars and disease diagnosis, they must be stable to perturbations in input and…
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 QUESO Library, User's Manual
Kemelli C. Estacio-Hiroms, Ernesto E. Prudencio, Nicholas P. Malaya +2
QUESO stands for Quantification of Uncertainty for Estimation, Simulation and Optimization and consists of algorithms and C++ classes intended for research in uncertainty quantific…
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…
Estimating Uncertainties in Statistics Computed from DNS
Todd A. Oliver, Nicholas Malaya, Rhys Ulerich +1
Rigorous assessment of uncertainty is crucial to the utility of DNS results. Uncertainties in the computed statistics arise from two sources: finite statistical sampling and the di…