Daniel Thomas Seidl

Scientific Machine Learning

Author profile picture

Scientific Machine Learning

dtseidl@sandia.gov

(505) 284-8679

Sandia National Laboratories, New Mexico
 P.O. Box 5800
 Albuquerque, NM 87185-1326

Biography

Tom has been at Sandia National Labs since January 2016. He develops computational methods in the areas of PDE-constrained optimization, finite elements, and uncertainty quantification.

Education

  • Ph.D. Mechanical Engineering, Boston University, August 2015
  • M.S. Mechanical Engineering, Boston University, May 2012
  • B.S. University of Rochester, Biomedical Engineering, May 2010

Publications

  • Geraci, G., Menhorn, F., 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. International Journal for Uncertainty Quantification, 14(3), pp. 21-65. https://doi.org/10.1615/int.j.uncertaintyquantification.2023048049 Publication ID: 123536
  • Ricciardi, D.E., Seidl, D.T., Lester, B., mc Jones, E., Jones, A.R., & Jones, A.R. (2023). Bayesian optimal experimental design for constitutive model calibration. International Journal of Mechanical Sciences, 265(265). https://doi.org/10.1016/j.ijmecsci.2023.108881 Publication ID: 123004
  • Granzow, B.N., Seidl, D.T., Bond, S.D., & Bond, S.D. (2023). Linearization errors in discrete goal-oriented error estimation [Conference Presenation]. Computer Methods in Applied Mechanics and Engineering. https://doi.org/10.2172/2430576 Publication ID: 125960
  • 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). https://doi.org/10.1016/j.cma.2023.116364 Publication ID: 123156
  • Debusschere, B.J., Curry, C.J., Seidl, D.T., Chang, K.W., Mariner, P.E., & Mariner, P.E. (2023). Machine Learning Surrogates for Fuel Degradation Processes in Nuclear Waste Repository Simulations [Conference Presenation]. https://doi.org/10.2172/2430768 Publication ID: 126624
  • Lester, B., Ricciardi, D.E., Seidl, D.T., Jones, A.R., mc Jones, E., & mc Jones, E. (2023). Interlaced Characterization and Calibration: Toward Actively Controlled, Optimal Experiments [Conference Presenation]. https://doi.org/10.2172/2430628 Publication ID: 126160
  • Seidl, D.T., Granzow, B.N., & Granzow, B.N. (2023). A Goal-oriented Approach to Model Form Error for Constitutive Models [Conference Presenation]. https://doi.org/10.2172/2430613 Publication ID: 126104
  • Ricciardi, D.E., Seidl, D.T., Karlson, K.N., & Karlson, K.N. (2023). Surrogates for Finite Element Simulations Enabling Efficient Material Model Calibration [Conference Presenation]. https://doi.org/10.2172/2430595 Publication ID: 126036
  • 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 Presenation]. https://doi.org/10.2172/2430524 Publication ID: 125776
  • Fayad, S.S., mc Jones, E., 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 Publication ID: 130660
  • mc Jones, E., Ricciardi, D.E., Seidl, D.T., Lester, B., Jones, A.R., & Jones, A.R. (2023). Interlaced Material Characterization and Model Calibration (ICC) [Conference Presenation]. https://doi.org/10.2172/2431303 Publication ID: 128548
  • Fayad, S.S., mc Jones, E., Seidl, D.T., Reu, P.L., Lambros, J., & Lambros, J. (2023). Considerations for the Identification of Elasto-Plastic Material Model Parameters [Conference Presenation]. https://doi.org/10.2172/2431011 Publication ID: 127492
  • Ricciardi, D.E., Seidl, D.T., mc Jones, E., Jones, A.R., Lester, B., & Lester, B. (2023). Bayesian Optimal Experimental Design for Material Model Calibration [Conference Poster]. https://doi.org/10.2172/2431296 Publication ID: 128520
  • Ricciardi, D.E., Seidl, D.T., Lester, B., mc Jones, E., Jones, A.R., & Jones, A.R. (2023). Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration [Conference Presenation]. https://doi.org/10.2172/2431753 Publication ID: 130020
  • Seidl, D.T., Ricciardi, D.E., Lester, B., Jones, A.R., mc Jones, E., & mc Jones, E. (2023). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Presenation]. https://doi.org/10.2172/2431988 Publication ID: 130884
  • Debusschere, B.J., Curry, C.J., Harvey, J.A., Seidl, D.T., Chang, K.W., Mariner, P.E., & Mariner, P.E. (2023). Machine Learning Surrogates for Time Dependent Fuel Degradation Processes in Nuclear Waste Repository Simulations [Conference Presenation]. https://doi.org/10.2172/2431917 Publication ID: 130656
  • Ricciardi, D.E., Seidl, D.T., Lester, B., mc Jones, E., Jones, A.R., & Jones, A.R. (2023). Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration [Conference Presenation]. https://doi.org/10.2172/2432143 Publication ID: 131364
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (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. https://doi.org/10.1080/00295450.2023.2197666 Publication ID: 123668
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Presenation]. https://doi.org/10.2172/2006027 Publication ID: 120840
  • 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 Presenation]. https://doi.org/10.2172/2006040 Publication ID: 120888
  • Fayad, S.S., Reu, P.L., mc Jones, E., 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 Presenation]. https://doi.org/10.2172/2006122 Publication ID: 121212
  • Ricciardi, D.E., Seidl, D.T., Lester, B., Jones, A.R., Kury, M., mc Jones, E., & mc Jones, E. (2022). Reduction of Full-Field Data by Spectral Decomposition [Conference Presenation]. https://doi.org/10.2172/2006107 Publication ID: 121152
  • Jakeman, J.D., Seidl, D.T., Gorodetsky, A., & Gorodetsky, A. (2022). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presenation]. https://doi.org/10.2172/2005475 Publication ID: 119336
  • Seidl, D.T., Ricciardi, D.E., Lester, B., Jones, A.R., mc Jones, E., & mc Jones, E. (2022). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Presenation]. https://doi.org/10.2172/2006095 Publication ID: 121104
  • 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. https://doi.org/10.2172/1888363 Publication ID: 80245
  • Ricciardi, D.E., Seidl, D.T., Lester, B., Jones, A.R., mc Jones, E., & mc Jones, E. (2022). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Poster]. https://doi.org/10.2172/2005388 Publication ID: 118992
  • Jakeman, J.D., Seidl, D.T., Gorodetsky, A., & Gorodetsky, A. (2022). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presenation]. https://doi.org/10.2172/2005378 Publication ID: 118956
  • Seidl, D.T., Ricciardi, D.E., Lester, B., Jones, A.R., mc Jones, E., & mc Jones, E. (2022). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models [Conference Poster]. https://doi.org/10.2172/2004296 Publication ID: 116400
  • 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. https://doi.org/10.1016/j.ijrmms.2022.105105 Publication ID: 80616
  • 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 Presenation]. https://doi.org/10.2172/2003612 Publication ID: 113760
  • Fayad, S.S., Seidl, D.T., Reu, P.L., mc Jones, E., Lambros, J., & Lambros, J. (2022). Finite Element Model Levelling for Material Model Calibration using Digital Image Correlation [Conference Presenation]. https://doi.org/10.2172/2003583 Publication ID: 113648
  • 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 Presenation]. https://doi.org/10.2172/2003512 Publication ID: 113368
  • 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 Presenation]. https://doi.org/10.2172/2003414 Publication ID: 112984
  • Seidl, D.T., Jakeman, J.D., & Jakeman, J.D. (2022). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presenation]. https://doi.org/10.2172/2003123 Publication ID: 111916
  • Menhorn, F., Geraci, G., Seidl, D.T., Marzouk, Y., Eldred, M., Bungartz, H., & Bungartz, H. (2022). Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty [Conference Presenation]. https://doi.org/10.2172/2002269 Publication ID: 110040
  • Swiler, L.P., Portone, T., Mariner, P.E., Leone, R., Brooks, D.M., Seidl, D.T., Debusschere, B.J., 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 Presenation]. https://doi.org/10.2172/2002228 Publication ID: 109876
  • Fayad, S.S., mc Jones, E., 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 Publication ID: 108180
  • 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. https://doi.org/10.1002/nme.6843 Publication ID: 76414
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (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 Publication ID: 114300
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (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 Publication ID: 116388
  • 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 Publication ID: 76127
  • Seidl, D.T., mc Jones, E., Lester, B., & Lester, B. (2021). Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility. https://doi.org/10.2172/1820000 Publication ID: 75609
  • Mariner, P.E., Berg, T.M., Debusschere, B.J., Eckert, A., Harvey, J.A., Laforce, T., 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. https://doi.org/10.2172/1825056 Publication ID: 76168
  • Seidl, D.T., Jakeman, J.D., & Jakeman, J.D. (2021). Improving Digital Twins by Learning from a Fleet of Assets [Conference Presenation]. https://doi.org/10.2172/1889023 Publication ID: 75878
  • Jakeman, J.D., Eldred, M., Geraci, G., Portone, T., Rushdi, A., Seidl, D.T., Smith, T.M., & Smith, T.M. (2021). Multi-fidelity Machine Learning [Conference Presenation]. https://doi.org/10.2172/1876608 Publication ID: 79162
  • 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 Presenation]. https://doi.org/10.2172/1884132 Publication ID: 79534
  • Menhorn, F., Geraci, G., Seidl, D.T., Eldred, M., King, R., Bungartz, H., Marzouk, Y., & Marzouk, Y. (2021). Multilevel Estimators for Measures of Robustness in Optimization under Uncertainty [Conference Presenation]. https://doi.org/10.2172/1881729 Publication ID: 79510
  • Bachman, W.B., Maniaci, D.C., Geraci, G., Seidl, D.T., Herges, T., Brown, K.A., Cutler, J.J., & Cutler, J.J. (2021). Application of Multifidelity Uncertainty Quantification Towards Multi-turbine Interaction and Wake Characterization [Conference Presenation]. https://doi.org/10.2172/1870979 Publication ID: 78674
  • Berg, T.M., Mariner, P.E., Debusschere, B.J., Seidl, D.T., Leone, R.C., Chang, K.W., & Chang, K.W. (2021). Machine Learning Surrogates for the Fuel Matrix Degradation Model [Presentation]. https://www.osti.gov/biblio/1869760 Publication ID: 78570
  • Berg, T.M., Chang, K.W., Leone, R.C., Seidl, D.T., Mariner, P.E., Debusschere, B.J., & Debusschere, B.J. (2021). Surrogate Modeling of Spent Fuel Degradation for Repository Performance Assessment [Conference Presenation]. https://doi.org/10.2172/1854307 Publication ID: 77449
  • Menhorn, F., Geraci, G., Seidl, D.T., Eldred, M., King, R., Bungartz, H., Marzouk, Y., & Marzouk, Y. (2021). Multifidelity Monte Carlo Estimators for Robust Formulations in Optimization under Uncertainty [Conference Presenation]. https://doi.org/10.2172/1847580 Publication ID: 77379
  • Fayad, S.S., mc Jones, E., Reu, P.L., Seidl, D.T., Lambros, J., & Lambros, J. (2021). Sensitivity-Based Simultaneous Experimentation and Calibration of Complex Elasto-Plastic Models [Conference Proceeding]. https://www.osti.gov/biblio/1847583 Publication ID: 77258
  • Menhorn, F., Geraci, G., Seidl, D.T., Eldred, M., King, R., Bungartz, H., Marzouk, Y., & Marzouk, Y. (2020). Multifidelity strategies for optimization under uncertainty of wind power plants [Conference Presenation]. https://doi.org/10.2172/1836901 Publication ID: 72258
  • Mariner, P.E., Nole, M.A., Basurto, E., Berg, T.M., Chang, K.W., Debusschere, B.J., Eckert, A., Ebeida, M., Gross, M., Hammond, G., Harvey, J.A., Jordan, S.H., Kuhlman, K.L., Laforce, T., Leone, R.C., McLendon, W., Mills, M.M., Park, H.D., Bachman, W.B., … Swiler, L.P. (2020). Advances in GDSA Framework Development and Process Model Integration. https://doi.org/10.2172/1671380 Publication ID: 100176
  • Mariner, P.E., Berg, T.M., Chang, K.W., Debusschere, B.J., Leone, R.C., Seidl, D.T., & Seidl, D.T. (2020). Surrogate Model Development of Spent Fuel Degradation for Repository Performance Assessment. https://doi.org/10.2172/1673178 Publication ID: 100244
  • Seidl, D.T., Granzow, B.N., & Granzow, B.N. (2020). Elastoplastic Constitutive Model Calibration with Automatic Differentiation-based Sensitivities [Conference Presenation]. https://doi.org/10.2172/1830965 Publication ID: 71066
  • Seidl, D.T., Song, D., Oberai, A.A., & Oberai, A.A. (2020). Three-dimensional traction microscopy accounting for cell-induced matrix degradation. Computer Methods in Applied Mechanics and Engineering, 364(C). https://doi.org/10.1016/j.cma.2020.112935 Publication ID: 66360
  • Dalbey, K., Eldred, M., Geraci, G., Jakeman, J.D., Maupin, K.A., Monschke, J.A., Seidl, D.T., Swiler, L.P., Bachman, W.B., Menhorn, F., Zeng, X., & Zeng, X. (2020). Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 Theory Manual. https://doi.org/10.2172/1630693 Publication ID: 106072
  • Adams, B.M., Bohnhoff, W.J., Dalbey, K., Ebeida, M., Eddy, J.P., Eldred, M., Hooper, R., Hough, P.D., Hu, K., Jakeman, J.D., Khalil, M., Maupin, K.A., Monschke, J.A., Ridgway, E.M., Rushdi, A., Seidl, D.T., Stephens, J.A., Swiler, L.P., Winokur, J., & Winokur, J. (2020). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User’s Manual. https://doi.org/10.2172/1630694 Publication ID: 106076
  • Maniaci, D.C., Bachman, W.B., Geraci, G., Seidl, D.T., Herges, T.G., Eldred, M., Blaylo, M.L., Houchens, B.C., & Houchens, B.C. (2020). Verification Validation and Uncertainty Quantification (V&V/UQ) of Wind Plant Models Project Overview of FY20 Q2 Milestone Completion: Wind Uncertainty Quantification Session and Publications [Presentation]. https://www.osti.gov/biblio/1778659 Publication ID: 73286
  • Fayad, S.S., Seidl, D.T., Reu, P.L., & Reu, P.L. (2020). Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization. Experimental Mechanics, 60(2), pp. 249-263. https://doi.org/10.1007/s11340-019-00553-9 Publication ID: 66256
  • Mariner, P.E., Debusschere, B.J., Hammond, G.E., Seidl, D.T., Laura, S., Vo, J., & Vo, J. (2020). Surrogate Modeling of Spatially Heterogeneous Source Terms for Probabilistic Assessment of Repository Performance [Conference Poster]. https://www.osti.gov/biblio/1763614 Publication ID: 70908
  • Bachman, W.B., Maniaci, D.C., Herges, T., Geraci, G., Seidl, D.T., Eldred, M., Blaylock, M., Houchens, B.C., & Houchens, B.C. (2020). Multilevel uncertainty quantification using cfd and openfast simulations of the swift facility [Conference Poster]. AIAA Scitech 2020 Forum. https://doi.org/10.2514/6.2020-1949 Publication ID: 70698
  • Friedrich, M., Geraci, G., Seidl, D.T., Eldred, M., Ryan, R., Hans-Joachim, B., Youssef, M., & Youssef, M. (2019). Higher moment multilevel estimators for optimization under uncertainty applied to wind plant design [Conference Poster]. https://www.osti.gov/biblio/1643359 Publication ID: 66951
  • Seidl, D.T., Oberai, A.A., Barbone, P.E., & Barbone, P.E. (2019). The Coupled Adjoint-State Equation in forward and inverse linear elasticity: Incompressible plane stress. Computer Methods in Applied Mechanics and Engineering, 357(C). https://doi.org/10.1016/j.cma.2019.112588 Publication ID: 70535
  • Fayad, S.S., Seidl, D.T., Reu, P.L., & Reu, P.L. (2019). Minimizing Pattern Induced Bias in Digital Image Correlation [Conference Poster]. https://www.osti.gov/biblio/1642830 Publication ID: 65655
  • Mariner, P.E., Debusschere, B.J., Jerden, J., Seidl, D.T., Swiler, L.P., Vo, J., & Vo, J. (2019). Lessons Learned in the Development of Source Term Surrogate Models for Repository Performance Assessment [Conference Poster]. https://www.osti.gov/biblio/1643084 Publication ID: 66069
  • Mariner, P.E., Connolly, L.A., Cunningham, L., Debusschere, B.J., Dobson, D.C., Frederick, J.M., Hammond, G.E., Jordan, S.H., Laforce, T., Nole, M.A., Park, H.D., Bachman, W.B., Rogers, R., Seidl, D.T., Sevougian, S.D., Stein, E., Swift, P., Swiler, L.P., Vo, J., Wallace, M., & Wallace, M. (2019). Progress in Deep Geologic Disposal Safety Assessment in the U.S. since 2010. https://doi.org/10.2172/1570094 Publication ID: 65476
  • Eldred, M., Geraci, G., Seidl, D.T., Menhorn, F., King, R., Herges, T., Bachman, W.B., Maniaci, D.C., & Maniaci, D.C. (2019). Milestone: Develop multilevel emulator-based Bayesian inference capabilities and demonstrate data assimilation for SWiFT configuration [Presentation]. https://www.osti.gov/biblio/1646013 Publication ID: 65367
  • Fayad, S.S., Reu, P.L., Seidl, D.T., & Seidl, D.T. (2019). Pattern Induced Bias in Digital Image Correlation [Conference Poster]. https://www.osti.gov/biblio/1640639 Publication ID: 68924
  • Mariner, P.E., Swiler, L.P., Seidl, D.T., Vo, J., & Vo, J. (2019). Lessons Learned in the Development of Source Term Surrogate Models for Repository Performance Assessment [Conference Poster]. https://www.osti.gov/biblio/1640670 Publication ID: 69032
  • Mariner, P.E., Seidl, D.T., Swiler, L.P., Debusschere, B.J., Vo, J., Jerden, J., Frederick, J.M., & Frederick, J.M. (2019). Surrogate Modeling of Fuel Dissolution [Presentation]. https://www.osti.gov/biblio/1648825 Publication ID: 68682
  • Seidl, D.T., van Bloemen Waanders, B.G., Wildey, T., & Wildey, T. (2019). Simultaneous inversion of shear modulus and traction boundary conditions in biomechanical imaging. Inverse Problems in Science and Engineering, 28(2), pp. 1-21. https://doi.org/10.1080/17415977.2019.1603222 Publication ID: 68059
  • Mariner, P.E., Swiler, L.P., Seidl, D.T., Debusschere, B.J., Vo, J., Frederick, J.M., & Frederick, J.M. (2019). High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance [Conference Poster]. https://www.osti.gov/biblio/1639277 Publication ID: 67301
  • Mariner, P.E., Swiler, L.P., Seidl, D.T., Debusschere, B.J., Vo, J., Frederick, J.M., & Frederick, J.M. (2019). High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance [Conference Poster]. https://www.osti.gov/biblio/1602117 Publication ID: 67105
  • Mariner, P.E., Swiler, L.P., Seidl, D.T., Debusschere, B.J., Vo, J., Frederick, J.M., Jerden, J.L., & Jerden, J.L. (2019). High fidelity surrogate modeling of fuel dissolution for probabilistic assessment of repository performance [Conference Poster]. International High-Level Radioactive Waste Management 2019, IHLRWM 2019. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85067129626&origin=inward Publication ID: 64431
  • Roach, R.A., Argibay, N., Allen, K., Balch, D., Beghini, L., Bishop, J.E., Boyce, B.L., Brown, J.A., Burchard, R.L., Chandross, M., Cook, A.W., Diantonio, C., Dressler, A.D., Forrest, E.C., Ford, K., Ivanoff, T., Jared, B.H., Johnson, K.L., Kammler, D., … Trembacki, B.L. (2018). Born Qualified Grand Challenge LDRD Final Report. https://doi.org/10.2172/1481619 Publication ID: 59393
  • Granzow, B.N., Seidl, D.T., & Seidl, D.T. (2018). Adjoint-based Calibration of Plasticity Model Parameters from Digital Image Correlation Data. https://doi.org/10.2172/1474264 Publication ID: 59095
  • mc Jones, E., Carroll, J.D., Karlson, K.N., Kramer, S., Lehoucq, R.B., Reu, P.L., Seidl, D.T., Turner, D.Z., & Turner, D.Z. (2018). High-Throughput Material Characterization using the Virtual Fields Method. https://doi.org/10.2172/1474817 Publication ID: 59110
  • Wildey, T., Butler, T., Jakeman, J.D., Seidl, D.T., van Bloemen Waanders, B.G., & van Bloemen Waanders, B.G. (2018). Data-informed Multiscale Modeling of Additive Materials [Conference Poster]. https://www.osti.gov/biblio/1523778 Publication ID: 62297
  • Seidl, D.T., van Bloemen Waanders, B.G., Wildey, T., & Wildey, T. (2018). Multiscale Interfaces for Large Scale Optimization [Conference Poster]. https://www.osti.gov/biblio/1525680 Publication ID: 61583
  • Roach, R.A., Jared, B.H., Cook, A.W., Keicher, D., van Bloemen Waanders, B.G., Swiler, L.P., Seidl, D.T., Wildey, T., Whetten, S.R., & Whetten, S.R. (2018). Born Qualified EAB Telecon [Presentation]. https://www.osti.gov/biblio/1514821 Publication ID: 60282
  • Seidl, D.T., Turner, D.Z., mc Jones, E., Karlson, K.N., Reu, P.L., & Reu, P.L. (2018). Optimal Mechanical Testing for Constitutive Parameter Identification [Conference Poster]. https://www.osti.gov/biblio/1498447 Publication ID: 60905
  • Wildey, T., van Bloemen Waanders, B.G., Seidl, D.T., & Seidl, D.T. (2017). Adaptive Multiscale Modeling Using Generalized Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1513505 Publication ID: 57313
  • Wildey, T., van Bloemen Waanders, B.G., Seidl, D.T., & Seidl, D.T. (2017). Multiscale Modeling Using Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1458195 Publication ID: 56724
  • Seidl, D.T., van Bloemen Waanders, B.G., Wildey, T., & Wildey, T. (2017). Multiscale Interfaces for Large Scale Optimization [Conference Poster]. https://www.osti.gov/biblio/1456522 Publication ID: 55715
  • van Bloemen Waanders, B.G., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2017). Multiscale optimization under uncertainty for additive manufacturing [Conference Poster]. https://www.osti.gov/biblio/1426379 Publication ID: 55236
  • van Bloemen Waanders, B.G., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2017). Multiscale optimization under uncertainty [Presentation]. https://www.osti.gov/biblio/1458249 Publication ID: 55002
  • Seidl, D.T., van Bloemen Waanders, B.G., Wildey, T., & Wildey, T. (2017). Simultaneous Estimation of Material Parameters and Neumann Boundary Conditions in a Linear Elastic Model by PDE-Constrained Optimization [Conference Poster]. https://www.osti.gov/biblio/1458297 Publication ID: 54920
  • Wildey, T., van Bloemen Waanders, B.G., Seidl, D.T., & Seidl, D.T. (2016). Uncertainty Quantification for Multiscale Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1368791 Publication ID: 50172
  • Wildey, T., van Bloemen Waanders, B.G., Seidl, D.T., Arbogast, T., Ganis, B., Girault, V., Pencheva, G., Wheeler, M.F., Xue, G., Yotov, I., Tavener, S., Vohralik, M., & Vohralik, M. (2016). Multiscale Mortar Methods: Theory Applications and Future Directions [Conference Poster]. https://www.osti.gov/biblio/1365248 Publication ID: 49573
  • van Bloemen Waanders, B.G., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2016). Multiscale Optimization Under Uncertainty [Conference Poster]. https://www.osti.gov/biblio/1348106 Publication ID: 48990
Showing 10 of 92 publications.