Jeremy Templeton
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
J Ling, A Kurzawski, J Templeton
Journal of Fluid Mechanics 807, 155-166, 2016
Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
J Ling, J Templeton
Physics of Fluids 27 (8), 085103, 2015
Model based design of a microfluidic mixer driven by induced charge electroosmosis
CK Harnett, J Templeton, KA Dunphy-Guzman, YM Senousy, MP Kanouff
Lab on a Chip 8 (4), 565-572, 2008
Machine learning strategies for systems with invariance properties
J Ling, R Jones, J Templeton
Journal of Computational Physics 318, 22-35, 2016
A toolbox of Hamilton-Jacobi solvers for analysis of nondeterministic continuous and hybrid systems
IM Mitchell, JA Templeton
International Workshop on Hybrid Systems: Computation and Control, 480-494, 2005
A material frame approach for evaluating continuum variables in atomistic simulations
JA Zimmerman, RE Jones, JA Templeton
Journal of Computational Physics 229 (6), 2364-2389, 2010
An atomistic-to-continuum coupling method for heat transfer in solids
GJ Wagner, RE Jones, JA Templeton, ML Parks
Computer Methods in Applied Mechanics and Engineering 197 (41-42), 3351-3365, 2008
Comparison of molecular dynamics with classical density functional and poisson–boltzmann theories of the electric double layer in nanochannels
JW Lee, RH Nilson, JA Templeton, SK Griffiths, A Kung, BM Wong
Journal of chemical theory and computation 8 (6), 2012-2022, 2012
An eddy-viscosity based near-wall treatment for coarse grid large-eddy simulation
JA Templeton, G Medic, G Kalitzin
Physics of fluids 17 (10), 105101, 2005
An efficient wall model for large-eddy simulation based on optimal control theory
JA Templeton, M Wang, P Moin
Physics of fluids 18 (2), 025101, 2006
A predictive wall model for large-eddy simulation based on optimal control techniques
JA Templeton, M Wang, P Moin
Physics of Fluids 20 (6), 065104, 2008
Comparison of molecular and primitive solvent models for electrical double layers in nanochannels
JW Lee, JA Templeton, KK Mandadapu, JA Zimmerman
Journal of chemical theory and computation 9 (7), 3051-3061, 2013
Electron transport enhanced molecular dynamics for metals and semi‐metals
RE Jones, JA Templeton, GJ Wagner, D Olmsted, NA Modine
International Journal for Numerical Methods in Engineering 83 (8‐9), 940-967, 2010
Predicting the mechanical response of oligocrystals with deep learning
AL Frankel, RE Jones, C Alleman, JA Templeton
Computational Materials Science 169, 109099, 2019
Wall modeling for LES of high Reynolds number channel flows: What turbulence information is retained?
G Kalitzin, G Medic, JA Templeton
Computers & fluids 37 (7), 809-815, 2008
A framework for near-wall RANS/LES coupling
G Medic, G Daeninck, JA Templeton, G Kalitzin
CTR Annual Research Briefs, 169-182, 2005
Atomistic and molecular effects in electric double layers at high surface charges
JW Lee, A Mani, JA Templeton
Langmuir 31 (27), 7496-7502, 2015
Model reduction with MapReduce-enabled tall and skinny singular value decomposition
PG Constantine, DF Gleich, Y Hou, J Templeton
SIAM Journal on Scientific Computing 36 (5), S166-S191, 2014
Application of a field-based method to spatially varying thermal transport problems in molecular dynamics
JA Templeton, RE Jones, GJ Wagner
Modelling and Simulation in Materials Science and Engineering 18 (8), 085007, 2010
Dependencies of the thermal conductivity of individual single-walled carbon nanotubes
JW Lee, AJ Meade, EV Barrera, JA Templeton
Proceedings of the Institution of Mechanical Engineers, Part N: Journal of …, 2010
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Articles 1–20