Publications

Frankel, A.L., Jones, R.E., Alleman, C., & Templeton, J.A. (2019). Predicting the mechanical response of oligocrystals with deep learning. Computational Materials Science, 169(C). 10.1016/j.commatsci.2019.109099

Jones, R.E., Boyce, B.L., Frankel, A., Heckman, N.M., Khalil, M., Ostien, J.T., Rizzi, F., Tachida, K., Teichert, G.H., & Templeton, J.A. (2019). Uncertainty Quantification of Microstructural Material Variability Effects. 10.2172/1814062

Frankel, A., Jones, R.E., Alleman, C., & Templeton, J.A. (2019). Predicting the mechanical response of oligocrystals using deep convolutional neural networks [Conference Poster]. https://www.osti.gov/biblio/1641050

Rizzi, F., Khalil, M., Jones, R.E., Templeton, J.A., Ostien, J.T., & Boyce, B.L. (2019). Bayesian modeling of inconsistent plastic response due to material variability. Computer Methods in Applied Mechanics and Engineering, 353(C). 10.1016/j.cma.2019.05.012

Jones, R.E., Rizzi, F., Templeton, J.A., Ostien, J.T., Alleman, C., Khalil, M., Frankel, A., Heckman, N.M., Boyce, B.L., Garikipati, K., & Teichert, G. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592996

Jones, R.E., Rizzi, F., Templeton, J.A., Ostien, J.T., Alleman, C., Khalil, M., Frankel, A., Heckman, N.M., Boyce, B.L., Garikipati, K., & Teichert, G. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592995

Jones, R.E., Rizzi, F., Templeton, J.A., Ostien, J.T., Alleman, C., Khalil, M., Frankel, A., Heckman, N.M., Boyce, B.L., Teichert, G., & Garikipati, K. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592993

Khalil, M., Rizzi, F., Frankel, A., Alleman, C., Templeton, J.A., Ostien, J.T., Boyce, B.L., & Jones, R.E. (2018). Embedded Model Error and Bayesian Model Selection for Material Variability [Conference Poster]. https://www.osti.gov/biblio/1508918

Salloum, M., Fabian, N.D., Hensinger, D.M., Lee, J., Allendorf, E.M., Bhagatwala, A., Blaylock, M.L., Chen, J.H., Templeton, J.A., & Tezaur, I.K. (2018). Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets. Data Science and Engineering, 3(1), pp. 1-23. https://doi.org/10.1007/s41019-017-0042-4

Debusschere, B., Templeton, J.A., Safta, C., Sargsyan, K., Pinar, A., & Najm, H.N. (2018). Predictive Fidelity of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1512381

Templeton, J.A., Sanders, C., & Ostien, J.T. (2018). Machine learning models of plastic flow based on representation theory. CMES - Computer Modeling in Engineering and Sciences, 117(3), pp. 309-342. 10.31614/cmes.2018.04285

Debusschere, B., Templeton, J.A., Safta, C., Sargsyan, K., Pinar, A., & Najm, H.N. (2017). Predictive Fidelity of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1513665

Erickson, L., Templeton, J.A., & Foulk, J.W. (2017). An Interpolative Particle Level Set Method for Interfacial Physics [Conference Poster]. https://www.osti.gov/biblio/1483155

Jones, R.E., Rizzi, F., Boyce, B.L., Templeton, J.A., & Ostien, J.T. (2017). Plasticity models of material variability based on uncertainty quantification techniques. Computer Methods in Applied Mechanics and Engineering. https://www.osti.gov/biblio/1429679

Erickson, L., Templeton, J.A., & Foulk, J.W. (2017). A Mesh-free Approach to Simulating Interfacial Multi-Physics Problems [Presentation]. https://www.osti.gov/biblio/1478722

Debusschere, B., Sadler, L.E., Antoun, B.R., Templeton, J.A., Kolda, T.G., & May, E. (2017). Improved Equity Diversity and Inclusion to Sustain an Effective Applied Mathematics Workforce [Conference Poster]. https://www.osti.gov/biblio/1466495

Debusschere, B., Pinar, A., Sargsyan, K., Templeton, J.A., & Najm, H.N. (2017). Predictive Fidelity Interpretability and Resilience of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1508933

Erickson, L., Templeton, J.A., & Foulk, J.W. (2017). Simulating Interfacial Multi-Physics Problems Using a Mesh-free Approach [Conference Poster]. https://www.osti.gov/biblio/1507070

Rao, R.R., Kucala, A., Noble, D.R., Wang, Y., Templeton, J.A., & Foulk, J.W. (2017). Computational Models for Molten Corium Flow and Reaction with Concrete [Conference Poster]. https://www.osti.gov/biblio/1506917

Jones, R.E., Zimmerman, J.A., Zhou, X., Templeton, J.A., & Garikipati, K. (2017). Modeling and Design with Material Variability due to Nano/microstructure [Conference Poster]. https://www.osti.gov/biblio/1458182

Rizzi, F., Boyce, B.L., Jones, R.E., Ostien, J.T., & Templeton, J.A. (2017). Bayesian Methods to Capture Inherent Material Variability in Additively Manufactured Samples [Conference Poster]. https://www.osti.gov/biblio/1456630

Safta, C., Blaylock, M.L., Templeton, J.A., Domino, S.P., Sargsyan, K., & Najm, H.N. (2017). Uncertainty quantification in LES of channel flow. International Journal for Numerical Methods in Fluids, 83(4), pp. 376-401. 10.1002/fld.4272

Ling, J., & Templeton, J.A. (2017). Machine Learning for Turbulence Modeling. 10.2172/1761814

Ling, J., Kurzawski, A., & Templeton, J.A. (2016). Deep Learning for Turbulence Modeling [Conference Poster]. https://www.osti.gov/biblio/1505474

Ling, J., Kurzawski, A., & Templeton, J.A. (2016). Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. Journal of Fluid Mechanics, 807, pp. 155-166. 10.1017/jfm.2016.615

Erickson, L., Templeton, J.A., & Foulk, J.W. (2016). A Mesh-free Method to Simulate Solid-to-Liquid Phase Transitions [Conference Poster]. https://www.osti.gov/biblio/1404781

Templeton, J.A., Erickson, L., & Foulk, J.W. (2016). A Mesh-Free Method to Predictively Simulate Solid-to-Liquid Phase Transitions in Abnormal Thermal Environments. 10.2172/1562815

Ling, J., Jones, R.E., & Templeton, J.A. (2016). Machine learning strategies for systems with invariance properties. Journal of Computational Physics, 318(C), pp. 22-35. 10.1016/j.jcp.2016.05.003

Erickson, L., Templeton, J.A., & Foulk, J.W. (2016). An Interpolative Particle Level Set Method [Conference Poster]. https://www.osti.gov/biblio/1373459

Erickson, L., Templeton, J.A., & Foulk, J.W. (2016). An Interpolative Particle Level Set Method [Conference Poster]. https://www.osti.gov/biblio/1373040

Ling, J., & Templeton, J.A. (2016). Machine Learning and Turbulent Flows: Learning from Turbulence Simulation Data Sets [Presentation]. https://www.osti.gov/biblio/1368896

Salloum, M., Fabian, N., Hensinger, D.M., Lee, J., Templeton, J.A., & Allendorf, E.M. (2016). Compressed Sensing and Reconstruction of Unstructured Mesh Datasets: Optimal Compression [Conference Poster]. https://www.osti.gov/biblio/1368786

Gittleson, F.S., Jones, R.E., Ward, D.K., Foster, M.E., Templeton, J.A., & Anstey, M. (2016). Modeling Species Diffusion and Electrolyte Interaction in Li-Air Batteries [Conference Poster]. https://www.osti.gov/biblio/1365116

Ling, J., & Templeton, J.A. (2015). Machine Learning for Uncertainty Quantification in Turbulent Flow Simulations [Conference Poster]. https://www.osti.gov/biblio/1338869

Ling, J., & Templeton, J.A. (2015). Machine Learning Models for Detection of Regions of High Model Form Uncertainty in RANS [Conference Poster]. https://www.osti.gov/biblio/1331933

Templeton, J.A., Blaylock, M.L., Domino, S.P., Hewson, J.C., Kumar, P.R., Ling, J., Najm, H.N., Ruiz, A., Safta, C., Sargsyan, K., Stewart, A., & Wagner, G. (2015). Calibration and Forward Uncertainty Propagation for Large-eddy Simulations of Engineering Flows. 10.2172/1221181

Templeton, J.A., Foulk, J.W., & Erickson, L. (2015). Overview of Meshfree Methods [Presentation]. https://www.osti.gov/biblio/1506760

Zimmerman, J.A., Jones, R.E., & Templeton, J.A. (2015). Principles of Coarse-graining and Coupling using the Atom-to-Continuum (AtC) Method. https://www.osti.gov/biblio/1226877

Ling, J., & Templeton, J.A. (2015). Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. Physics of Fluids, 27(8). 10.1063/1.4927765

Templeton, J.A., Safta, C., Blaylock, M., Hewson, J., Domino, S., & Kumar, P. (2015). Calibration and Forward Uncertainty Propagation of Turbulence Models for Coarse-Grid Large-Eddy Simulation [Conference Poster]. https://www.osti.gov/biblio/1339221

Templeton, J.A., Lee, J., & Mani, A. (2015). Atomistic and molecular effects in electric double layers at high surface charges. Langmuir, 31(27). 10.1021/acs.langmuir.5b00215

Salloum, M., Fabian, N., Hensinger, D.M., & Templeton, J.A. (2015). Compressed Sensing and Reconstruction of Unstructured Mesh Datasets [Conference Poster]. https://www.osti.gov/biblio/1331610

Blaylock, M.L., Safta, C., & Templeton, J.A. (2015). A Novel Approach to Wall Model Treatment for Large Eddy Simulations [Conference Poster]. https://www.osti.gov/biblio/1253298

Blaylock, M.L., Templeton, J.A., Safta, C., Hewson, J.C., & Domino, S.P. (2015). Model Calibration and Forward Uncertainty Quantification for Large-Eddy Simulation of Turbulent Flows [Conference Poster]. https://www.osti.gov/biblio/1245792

Jones, R.E., Ward, D.K., & Templeton, J.A. (2014). Spatial resolution of the electrical conductance of ionic fluids using a Green-Kubo method. Journal of Chemical Physics, 141(18). 10.1063/1.4901035

Blaylock, M.L., Templeton, J.A., & Safta, C. (2014). Model Calibration and Forward Uncertainty Quantification for Large-Eddy Simulation of Turbulent Flows [Presentation]. https://www.osti.gov/biblio/1504047

Ward, D.K., Jones, R.E., Templeton, J.A., Reyes, K.R., & Kane, M. (2014). Improved Parameterization of Ethylene Carbonate Model for Density and Transition Temperature [Conference Poster]. https://www.osti.gov/biblio/1315219

Jones, R.E., Templeton, J.A., Reyes, K.R., Erickson, K., & Ward, D.K. (2014). Improving Li-Air batteries by accurately predicting lithium diffusion coefficients [Conference]. https://www.osti.gov/biblio/1145515

Blaylock, M.L., Safta, C., & Templeton, J.A. (2014). Rigorous LES Assessment for Predictive Simulations [Presentation]. https://www.osti.gov/biblio/1685007

Scott, S.N., Templeton, J.A., Ruthruff, J., Hough, P.D., & Peterson, J.P. (2014). Computational solution verification and validation applied to a thermal model of a ruggedized instrumentation package. WIT Transactions on Modelling and Simulation, 55. https://doi.org/10.2495/CMEM130021