J
Jeremy Alan Templeton
Researcher at Sandia National Laboratories
Publications - 57
Citations - 2909
Jeremy Alan Templeton is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Turbulence & Reynolds number. The author has an hindex of 16, co-authored 57 publications receiving 2170 citations. Previous affiliations of Jeremy Alan Templeton include Center for Turbulence Research & Stanford University.
Papers
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Journal ArticleDOI
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
TL;DR: This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data and proposes a novel neural network architecture which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropic tensor.
Journal ArticleDOI
Machine learning strategies for systems with invariance properties
TL;DR: This paper will specifically address physical systems that possess symmetry or invariance properties and shows that in both cases embedding the invariance property into the input features yields higher performance at significantly reduced computational training costs.
Journal ArticleDOI
Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
TL;DR: Machine learning algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions.
Book ChapterDOI
A toolbox of hamilton-jacobi solvers for analysis of nondeterministic continuous and hybrid systems
TL;DR: This paper describes the first publicly available toolbox for approximating the solution of Hamilton-Jacobi partial differential equations, and discusses three examples of how these equations can be used in system analysis: cost to go, stochastic differential games, and Stochastic hybrid systems.
Journal ArticleDOI
Model based design of a microfluidic mixer driven by induced charge electroosmosis
Cindy K. Harnett,Jeremy Alan Templeton,Katherine Dunphy-Guzman,Y. M. Senousy,Michael P. Kanouff +4 more
TL;DR: A mixer for microfluidic sample preparation based on the electrokinetic phenomenon of induced-charge-electroosmosis (ICEO), which enables mixing to be turned on or off within a channel of fixed volume to prevent sample dilution.