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
Machine learning strategies for systems with invariance properties
J Ling, R Jones, J Templeton
Journal of Computational Physics 318, 22-35, 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
A toolbox of Hamilton-Jacobi solvers for analysis of nondeterministic continuous and hybrid systems
IM Mitchell, JA Templeton
Hybrid Systems: Computation and Control: 8th International Workshop, HSCC …, 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
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 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
Predicting the mechanical response of oligocrystals with deep learning
AL Frankel, RE Jones, C Alleman, JA Templeton
Computational Materials Science 169, 109099, 2019
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
Machine learning models of plastic flow based on representation theory
RE Jones, JA Templeton, CM Sanders, JT Ostien
arXiv preprint arXiv:1809.00267, 2018
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
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
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
A long-range electric field solver for molecular dynamics based on atomistic-to-continuum modeling
JA Templeton, RE Jones, JW Lee, JA Zimmerman, BM Wong
Journal of Chemical Theory and Computation 7 (6), 1736-1749, 2011
Uncertainty quantification in LES of channel flow
C Safta, M Blaylock, J Templeton, S Domino, K Sargsyan, H Najm
International Journal for Numerical Methods in Fluids 83 (4), 376-401, 2017
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
Inference and uncertainty propagation of atomistically informed continuum constitutive laws, part 2: Generalized continuum models based on gaussian processes
M Salloum, JA Templeton
International Journal for Uncertainty Quantification 4 (2), 2014
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Articles 1–20