Publications

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P., & Mariner, P. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Presentation]. 10.2172/2006027

Seidl, D.T., Granzow, B.N., & Granzow, B.N. (2022). Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data [Conference Presentation]. 10.2172/2006040

Fayad, S.S., Reu, P.L., Jones, E.M.C., Seidl, D.T., Lambros, J., & Lambros, J. (2022). Direct-Levelling Finite Element Analysis Data for Material Model Calibration using Digital Image Correlation and Finite Element Model Updating [Conference Presentation]. 10.2172/2006122

Jakeman, J.D., Seidl, D.T., Gorodetsky, A., & Gorodetsky, A. (2022). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presentation]. 10.2172/2005475

Jakeman, J.D., Eldred, M., Geraci, G., Seidl, D.T., Smith, T.M., Gorodetsky, A.A., Pham, T., Narayan, A., Zeng, X., Ghanem, R., & Ghanem, R. (2022). Multi-fidelity information fusion and resource allocation. 10.2172/1888363

Seidl, D.T., Ricciardi, D., Lester, B.T., Jones, A.R., Jones, E.M.C., & Jones, E.M.C. (2022). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Presentation]. 10.2172/2006095

Jakeman, J.D., Seidl, D.T., Gorodetsky, A., & Gorodetsky, A. (2022). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presentation]. 10.2172/2005378

Ricciardi, D., Seidl, D.T., Lester, B.T., Jones, A.R., Jones, E.M.C., & Jones, E.M.C. (2022). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Poster]. https://doi.org/10.2172/2005388

Seidl, D.T., Ricciardi, D., Lester, B.T., Jones, A.R., Jones, E.M.C., & Jones, E.M.C. (2022). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Poster]. 10.2172/2004296

Seidl, D.T., Granzow, B.N., & Granzow, B.N. (2022). Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data [Conference Presentation]. 10.2172/2003612

Seidl, D.T., Valiveti, D.M., & Valiveti, D.M. (2022). Peridynamics and surrogate modeling of pressure-driven well stimulation. International Journal of Rock Mechanics and Mining Sciences, 154. 10.1016/j.ijrmms.2022.105105

Fayad, S.S., Seidl, D.T., Reu, P.L., Jones, E.M.C., Lambros, J., & Lambros, J. (2022). Finite Element Model Levelling for Material Model Calibration using Digital Image Correlation [Conference Presentation]. 10.2172/2003583

Menhorn, F., Geraci, G., Seidl, D.T., King, R., Eldred, M., Bungartz, H., Marzouk, Y., & Marzouk, Y. (2022). Multilevel Monte Carlo derivative-free optimization under uncertainty of wind power plants [Conference Presentation]. 10.2172/2003512

Stephens, J.A., Seidl, D.T., Adams, B.M., Geraci, G., & Geraci, G. (2022). Overview of the latest features and capabilities in the Dakota software [Conference Presentation]. 10.2172/2003414

Swiler, L.P., Portone, T., Mariner, P., Leone, R., Brooks, D.M., Seidl, D.T., Debusschere, B., Berg, T., & Berg, T. (2022). Use of a machine learning model for a constitutive chemistry model within a groundwater flow and transport application modeling nuclear fuel degradation in a waste repository [Conference Presentation]. 10.2172/2002228

Seidl, D.T., Jakeman, J.D., & Jakeman, J.D. (2022). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presentation]. 10.2172/2003123

Fayad, S.S., Jones, E.M.C., Reu, P.L., Seidl, D.T., Lambros, J., & Lambros, J. (2022). Levelling of Finite Element Models for Material Model Calibration using Digital Image Correlation [Conference Proceeding]. https://www.osti.gov/biblio/2001774

Seidl, D.T., Granzow, B.N., & Granzow, B.N. (2022). Calibration of elastoplastic constitutive model parameters from full-field data with automatic differentiation-based sensitivities. International Journal for Numerical Methods in Engineering, 123(1), pp. 69-100. 10.1002/nme.6843

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P., & Mariner, P. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Paper]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181567561&origin=inward

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P., & Mariner, P. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Paper]. Proceedings of the International High Level Radioactive Waste Management Conference Ihlrwm 2022 Embedded with the 2022 Ans Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181567561&origin=inward

Eldred, M., Geraci, G., Gorodetsky, A.A., Jakeman, J.D., Portone, T., Wildey, T., Rushdi, A., Seidl, D.T., & Seidl, D.T. (2021). The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact [Presentation]. https://www.osti.gov/biblio/1891078

Mariner, P., Berg, T.M., Debusschere, B., Eckert, A., Harvey, J.A., Laforce, T.C., Leone, R.C., Mills, M.M., Nole, M.A., Park, H.D., Perry, F.V., Seidl, D.T., Swiler, L.P., Chang, K.W., & Chang, K.W. (2021). GDSA Framework Development and Process Model Integration FY2021. 10.2172/1825056

Seidl, D.T., Jones, E.M.C., Lester, B.T., & Lester, B.T. (2021). Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility. 10.2172/1820000

Seidl, D.T., Jakeman, J.D., & Jakeman, J.D. (2021). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presentation]. 10.2172/1889023

Seidl, D.T., Granzow, B.N., & Granzow, B.N. (2021). Calibration of Elastoplastic Constitutive Model Parameters from Full-Field Data with Automatic Differentiation-based Sensitivities [Conference Presentation]. 10.2172/1884132