Publications

Jones, E.M.C., Reu, P.L., Kramer, S.L.B., Jones, A.R., Carroll, J.D., Karlson, K.N., Seidl, D.T., Turner, D.Z., & Turner, D.Z. (2024). Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel. Scientific Data, 11(1). 10.1038/s41597-024-03949-y

Fuhg, J.N., Jadoon, A., Seidl, D.T., Jones, R.E., & Jones, R.E. (2024). Polyconvex neural network models of thermoelasticity. Journal of the Mechanics and Physics of Solids, 192(1). 10.1016/j.jmps.2024.105837

Jones, E.M.C., Ricciardi, D., Seidl, D.T., Lester, B.T., Jones, A.R., Swanson, M.E., & Swanson, M.E. (2024). Interlaced Material Characterization and Model Calibration (ICC) for Improved Computational Simulation [Conference Presentation]. 10.2172/2585131

Ricciardi, D., Seidl, D.T., Lester, B.T., Jones, A.R., Jones, E.M.C., Swanson, M.E., & Swanson, M.E. (2024). Interlaced Characterization and Calibration: In-situ Bayesian optimal experimental design for constitutive model calibration [Conference Presentation]. 10.2172/2566698

Seidl, D.T., Granzow, B.N., Jones, R.E., Patel, R., & Patel, R. (2024). Calibration of Hybrid Constitutive Models from Full-field Data [Presentation]. 10.2172/2585215

Menhorn, F., Geraci, G., Seidl, D.T., Marzouk, Y.M., Eldred, M., Bungartz, H.J., & Bungartz, H.J. (2024). MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY [Conference Presentation]. International Journal for Uncertainty Quantification. 10.2172/2002269

Geraci, G., Menhorn, F., Seidl, D.T., Marzouk, Y.M., Eldred, M., Bungartz, H., & Bungartz, H. (2023). Multilevel Monte Carlo Estimators For Derivative-Free Optimization Under Uncertainty. International Journal for Uncertainty Quantification, 14(3). 10.1615/int.j.uncertaintyquantification.2023048049

Ricciardi, D., Seidl, D.T., Lester, B.T., Jones, E.M.C., Jones, A.R., & Jones, A.R. (2023). Bayesian optimal experimental design for constitutive model calibration. International Journal of Mechanical Sciences, 265(265). 10.1016/j.ijmecsci.2023.108881

Granzow, B.N., Seidl, D.T., Bond, S.D., & Bond, S.D. (2023). Linearization errors in discrete goal-oriented error estimation [Conference Presentation]. Computer Methods in Applied Mechanics and Engineering. 10.2172/2430576

Granzow, B.N., Seidl, D.T., Bond, S.D., & Bond, S.D. (2023). Linearization errors in discrete goal-oriented error estimation. Computer Methods in Applied Mechanics and Engineering, 416(416). 10.1016/j.cma.2023.116364

Debusschere, B., Curry, C.J., Seidl, D.T., Chang, K.W., Mariner, P., & Mariner, P. (2023). Machine Learning Surrogates for Fuel Degradation Processes in Nuclear Waste Repository Simulations [Conference Presentation]. 10.2172/2430768

Jakeman, J.D., Perego, M., Seidl, D.T., Hillebrand, T., Hoffman, M., Price, S., & Price, S. (2023). Ice Sheet Models of Different Fidelity for Uncertainty Quantification [Conference Presentation]. 10.2172/2430524

Ricciardi, D., Seidl, D.T., Karlson, K.N., & Karlson, K.N. (2023). Surrogates for Finite Element Simulations Enabling Efficient Material Model Calibration [Conference Presentation]. 10.2172/2430595

Seidl, D.T., Granzow, B.N., & Granzow, B.N. (2023). A Goal-oriented Approach to Model Form Error for Constitutive Models [Conference Presentation]. 10.2172/2430613

Lester, B.T., Ricciardi, D., Seidl, D.T., Jones, A.R., Jones, E.M.C., & Jones, E.M.C. (2023). Interlaced Characterization and Calibration: Toward Actively Controlled, Optimal Experiments [Conference Presentation]. 10.2172/2430628

Fayad, S.S., Jones, E.M.C., Seidl, D.T., Reu, P.L., Lambros, J., & Lambros, J. (2023). Considerations for the Identification of Elasto-Plastic Material Model Parameters [Conference Paper]. https://www.osti.gov/biblio/2431918

Jones, E.M.C., Ricciardi, D., Seidl, D.T., Lester, B.T., Jones, A.R., & Jones, A.R. (2023). Interlaced Material Characterization and Model Calibration (ICC) [Conference Presentation]. 10.2172/2431303

Fayad, S.S., Jones, E.M.C., Seidl, D.T., Reu, P.L., Lambros, J., & Lambros, J. (2023). Considerations for the Identification of Elasto-Plastic Material Model Parameters [Conference Presentation]. https://doi.org/10.2172/2431011

Ricciardi, D., Seidl, D.T., Jones, E.M.C., Jones, A.R., Lester, B.T., & Lester, B.T. (2023). Bayesian Optimal Experimental Design for Material Model Calibration [Conference Poster]. 10.2172/2431296

Ricciardi, D., Seidl, D.T., Lester, B.T., Jones, E.M.C., Jones, A.R., & Jones, A.R. (2023). Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration [Conference Presentation]. 10.2172/2431753

Seidl, D.T., Ricciardi, D., Lester, B.T., Jones, A.R., Jones, E.M.C., & Jones, E.M.C. (2023). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Presentation]. 10.2172/2431988

Debusschere, B., Curry, C.J., Harvey, J.A., Seidl, D.T., Chang, K.W., Mariner, P., & Mariner, P. (2023). Machine Learning Surrogates for Time Dependent Fuel Degradation Processes in Nuclear Waste Repository Simulations [Conference Presentation]. 10.2172/2431917

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P., & Mariner, P. (2023). Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository. Nuclear Technology, 209(9), pp. 1295-1318. 10.1080/00295450.2023.2197666

Ricciardi, D., Seidl, D.T., Lester, B.T., Jones, E.M.C., Jones, A.R., & Jones, A.R. (2022). Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration [Conference Presentation]. 10.2172/2432143

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