activity
20152021
most citedDeep learning for presumed probability density function models

68 citations · 117 across the 9 of their papers we have counts for

collaborators

9 papers

physics.flu-dyn2021★ 24 cited

A Moving Embedded Boundary Approach For The Compressible Navier-Stokes Equations In A Block-Structured Adaptive Refinement Framework

Mahesh Natarajan, Ray Grout, Weiqun Zhang +1

A computational technique has been developed to perform compressible flow simulations involving moving boundaries using an embedded boundary approach within the block-structured ad…

quant-ph2021

Energy use in quantum data centers: Scaling the impact of computer architecture, qubit performance, size, and thermal parameters

Michael James Martin, Caroline Hughes, Gilberto Moreno +4

As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum com…

cs.LG2019

A block-random algorithm for learning on distributed, heterogeneous data

Prakash Mohan, Marc T. Henry de Frahan, Ryan King +1

Most deep learning models are based on deep neural networks with multiple layers between input and output. The parameters defining these layers are initialized using random values…

physics.flu-dyn2019★ 4 cited

Data recovery in computational fluid dynamics through deep image priors

Marc T. Henry de Frahan, Ray W. Grout

One of the challenges encountered by computational simulations at exascale is the reliability of simulations in the face of hardware and software faults. These faults, expected to…

physics.comp-ph2019★ 68 cited

Deep learning for presumed probability density function models

Marc T. Henry de Frahan, Shashank Yellapantula, Ryan King +2

In this work, we use ML techniques to develop presumed PDF models for large eddy simulations of reacting flows. The joint sub-filter PDF of mixture fraction and progress variable i…

math.NA2018

A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters

John Bell, Marcus Day, Jonathan Goodman +2

First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifi…

physics.comp-ph2018

An adaptive timestepping methodology for particle advance in coupled CFD-DEM simulations

Hariswaran Sitaraman, Ray Grout

An adpative integration technique for time advancement of particle motion in the context of coupled computational fluid dynamics (CFD) - discrete element method (DEM) simulations i…

math.NA2016★ 1 cited

Iterative importance sampling algorithms for parameter estimation

Matthias Morzfeld, Marcus S. Day, Ray W. Grout +3

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov Chain Mont…

cs.CE2015★ 20 cited

Achieving algorithmic resilience for temporal integration through spectral deferred corrections

R. W. Grout, H. Kolla, M. L. Minion +1

Spectral deferred corrections (SDC) is an iterative approach for constructing higher- order accurate numerical approximations of ordinary differential equations. SDC starts with an…