Publications

Frankel, A.L., Jones, R.E., Alleman, C., Templeton, J.A., & Templeton, J.A. (2019). Predicting the mechanical response of oligocrystals with deep learning. Computational Materials Science, 169(C). https://doi.org/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., & Templeton, J.A. (2019). Uncertainty Quantification of Microstructural Material Variability Effects. https://doi.org/10.2172/1814062

Frankel, A., Jones, R.E., Alleman, C., Templeton, J.A., & 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., & Boyce, B.L. (2019). Bayesian modeling of inconsistent plastic response due to material variability. Computer Methods in Applied Mechanics and Engineering, 353(C), pp. 183-200. https://doi.org/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., Teichert, G., Garikipati, K., & Garikipati, K. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592993

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., & 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., Garikipati, K., Teichert, G., & Teichert, G. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592996

Khalil, M., Rizzi, F., Frankel, A., Alleman, C., Templeton, J.A., Ostien, J.T., Boyce, B.L., Jones, R.E., & 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., Chen, J., Templeton, J.A., Tezaur, I.K., & 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

Templeton, J.A., Sanders, C., Ostien, J.T., & 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. https://doi.org/10.31614/cmes.2018.04285

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

Debusschere, B.J., Templeton, J.A., Safta, C., Sargsyan, K., Pinar, A., Najm, H.N., & Najm, H.N. (2018). Predictive Fidelity of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1513665

Jones, R.E., Rizzi, F., Boyce, B.L., Templeton, J.A., Ostien, J.T., & 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., Bachman, W.B., & Bachman, W.B. (2017). An Interpolative Particle Level Set Method for Interfacial Physics [Conference Poster]. https://www.osti.gov/biblio/1483155

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

Debusschere, B.J., Pinar, A., Sargsyan, K., Templeton, J.A., Najm, H.N., & 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

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

Erickson, L., Templeton, J.A., Bachman, W.B., & Bachman, W.B. (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., Bachman, W.B., & Bachman, W.B. (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., & 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., & 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., Templeton, J.A., Domino, S.P., Sargsyan, K., Najm, H.N., & Najm, H.N. (2017). Uncertainty quantification in LES of channel flow. International Journal for Numerical Methods in Fluids, 83(4), pp. 376-401. https://doi.org/10.1002/fld.4272

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

Ling, J., Kurzawski, A., Templeton, J.A., & Templeton, J.A. (2017). Deep Learning for Turbulence Modeling [Conference Poster]. https://www.osti.gov/biblio/1505474

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