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

Denielle Ricciardi, Daniel Thomas Seidl, Brian T. Lester, Amanda Jones, Matthew Wilson Kury, Elizabeth M. C. Jones, (2022). Reduction of Full-Field Data by Spectral Decomposition International Digital Image Correlation Society Conference Document ID: 1573821

Samuel Saiid Fayad, Elizabeth M. C. Jones, Daniel Thomas Seidl, Phillip L. Reu, John Lambros, (2022). Direct-Levelling Finite Element Analysis Data for Material Model Calibration using Digital Image Correlation and Finite Element Model Updating IDICs Annual Conference Document ID: 1562903

Daniel Thomas Seidl, Brian Neal Granzow, (2022). Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data International Digital Image Correlation Society Annual Meeting Document ID: 1573591

Denielle Ricciardi, Daniel Thomas Seidl, Brian T. Lester, Amanda Jones, Elizabeth M. C. Jones, (2022). Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration Tms Document ID: 1551708

Bert Debusschere, Daniel Thomas Seidl, Timothy M. Berg, Kyung Won Chang, Rosemary Claire Leone, Laura Painton Swiler, Paul Mariner, (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository International High-Level Radioactive Waste Management Conference Document ID: 1551402

Daniel Thomas Seidl, Brian Neal Granzow, (2022). Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data 19th U.S. National Congress on Theoretical and Applied Mechanics Document ID: 1540770

Samuel Saiid Fayad, Daniel Thomas Seidl, Phillip L. Reu, Elizabeth M. C. Jones, John Lambros, (2022). Finite Element Model Levelling for Material Model Calibration using Digital Image Correlation Society for Experimental Mechanics Annual Conference Document ID: 1540720

Alan Hsieh, David Charles Maniaci, Thomas Herges, Gianluca Geraci, Daniel Thomas Seidl, (2022). Application of Forward Multifidelity Uncertainty Quantification to Wind Farms AIAA SciTech 2023 Document ID: 1540616

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Ryan King, Michael S. Eldred, Hans-Joachim Bungartz, Youssef Marzouk, (2022). Multilevel Monte Carlo derivative-free optimization under uncertainty of wind power plants 8th European Congress on Computational Methods in Applied Sciences and Engineering Document ID: 1540542

Bert Debusschere, Timothy M. Berg, Daniel Thomas Seidl, Kyung Won Chang, Rosemary Claire Leone, Laura Painton Swiler, Paul Mariner, (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository DOE Computational Research Leadership Council (CRLC) Seminar Series Document ID: 1540286

Denielle Ricciardi, Daniel Thomas Seidl, Brian T. Lester, Amanda Jones, Elizabeth M. C. Jones, (2022). Interlaced Characterization and Calibration: Oline Bayesian Optimal Experimental Design for Constitutive Model Calibration AIAA SciTech Document ID: 1539868

John Adam Stephens, Daniel Thomas Seidl, Brian M. Adams, Gianluca Geraci, (2022). Overview of the latest features and capabilities in the Dakota software 2022 ECCOMAS Congress Document ID: 1539822

Daniel Thomas Seidl, John Davis Jakeman, (2022). Improving Digital Twins by Learning from a Fleet of Assets SIAM Conference on Uncertainty Quantification Document ID: 1505152

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Youssef Marzouk, Michael S. Eldred, Hans-Joachim Bungartz, (2022). Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty Siam Uq 22 Document ID: 1504921

Daniel Thomas Seidl, Dakshina M. Valiveti, (2022). Peridynamics and Surrogate Modeling of Pressure-driven Well Stimulation International Journal of Rock Mechanics and Mining Sciences https://www.osti.gov/search/identifier:1870476 Document ID: 1494314

Laura Painton Swiler, Teresa Portone, Paul Mariner, Rosie Leone, Dusty Marie Brooks, Daniel Thomas Seidl, Bert Debusschere, Tim Berg, (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 Society of Industrial and Applied Mathematics(SIAM) Conference on Uncertainty Quantification Document ID: 1494220

Daniel Thomas Seidl, Brian T. Lester, Elizabeth M. C. Jones, Denielle Ricciardi, Amanda Jones, (2022). Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration SIAM Conference on Mathematics of Data Science Document ID: 1493135

Daniel Thomas Seidl, Denielle Ricciardi, Brian T. Lester, Amanda Jones, Elizabeth M. C. Jones, (2022). Interlaced Characterization and Calibration of Elastoplastic Constitutive Models SIAM Conference on the Mathematics of Data Science Document ID: 1493046

Bert Debusschere, Timothy M. Berg, Daniel Thomas Seidl, Kyung Won Chang, Rosemary Claire Leone, Laura Painton Swiler, Paul Mariner, (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository 2022 International High Level Radioactive Waste Management Conference Document ID: 1493003

Samuel Saiid Fayad, Elizabeth M. C. Jones, Phillip L. Reu, Daniel Thomas Seidl, John Lambros, (2022). Levelling of Finite Element Models for Material Model Calibration using Digital Image Correlation Society for Experimental Mechanics Annual Conference Document ID: 1459732

Daniel Thomas Seidl, Brian Neal Granzow, (2022). Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data 19th U.S. National Congress on Theoretical and Applied Mechanics Document ID: 1438321

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Ryan King, Michael S. Eldred, Hans-Joachim Bungartz, Youssef Marzouk, (2022). Multilevel Monte Carlo derivative-free optimization under uncertainty of wind power plants 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) Document ID: 1438392

John Adam Stephens, Daniel Thomas Seidl, Brian M. Adams, Gianluca Geraci, (2021). Overview Of The Latest Features And Capabilities In The Dakota Software 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) Document ID: 1405439

Daniel Thomas Seidl, Brian Neal Granzow, (2021). Calibration of Elastoplastic Constitutive Model Parameters from Full-field Data with Automatic Differentiation-based Sensitivities International Journal for Numerical Methods in Engineering https://www.osti.gov/search/identifier:1828784 Document ID: 1369123

Daniel Thomas Seidl, John Davis Jakeman, (2021). Improving Digital Twins by Learning from a Fleet of Assets SIAM Conference on Uncertainty Quantification (UQ22) Document ID: 1369604

Samuel Saiid Fayad, Elizabeth M. C. Jones, Daniel Thomas Seidl, Phillip L. Reu, John Lambros, (2021). Minimizing Model calibration Error from Full-Field Diagnostics Society for Experimental Mechanics Annual Conference Document ID: 1369353

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Youssef Marzouk, Michael S. Eldred, Hans-Joachim Bungartz, (2021). Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty SIAM Conference on Uncertainty Quantification (UQ22) Document ID: 1369674

Paul Mariner, Timothy M. Berg, Bert Debusschere, Aubrey Celia Eckert, Jacob Harvey, Tara LaForce, Rosemary Claire Leone, Melissa Marie Mills, Michael Anthony Nole, Heeho Daniel Park, F.V. Perry, Daniel Thomas Seidl, Laura Painton Swiler, Kyung Won Chang, (2021). GDSA Framework Development and Process Model Integration FY2021 https://www.osti.gov/search/identifier:1825056 Document ID: 1368725

Laura Painton Swiler, Teresa Portone, Paul Mariner, Rosemary Claire Leone, Dusty Marie Brooks, Daniel Thomas Seidl, Bert Debusschere, Timothy M. Berg, (2021). 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 SIAM Conference on Uncertainty Quantification (UQ22) Document ID: 1369039

Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Teresa Portone, Timothy Michael Wildey, Ahmad Rushdi, Daniel Thomas Seidl, (2021). The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact NASA Langley HPC Seminar Series Document ID: 1369333

Daniel Thomas Seidl, John Davis Jakeman, (2021). Improving Digital Twins by Learning from a Fleet of Assets Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Document ID: 1368070

Daniel Thomas Seidl, Elizabeth M. C. Jones, Brian T. Lester, (2021). Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility https://www.osti.gov/search/identifier:1820000 Document ID: 1357085

Alan Hsieh, David Charles Maniaci, Thomas Herges, Gianluca Geraci, Daniel Thomas Seidl, Ryan King, (2021). Multifidelity Uncertainty Quantification for Wind Farm Turbines and Wakes Torque 2022 Document ID: 1357249

Daniel Thomas Seidl, Brian M. Adams, John Adam Stephens, Gianluca Geraci, (2021). Dakota Software For Optimization, Uncertainty Quantification And Model Calibration Eccomas 2022 Document ID: 1344547

Daniel Thomas Seidl, Brian Neal Granzow, (2021). Calibration of Elastoplastic Constitutive Model Parameters from Full-Field Data with Automatic Differentiation-based Sensitivities 16th U.S. National Congress on Computational Mechanics Document ID: 1332050

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, Ryan King, Hans-Joachim Bungartz, Youssef Marzouk, (2021). Multilevel Estimators for Measures of Robustness in Optimization under Uncertainty 16th US National Congress on Computational Mechanics Document ID: 1342662

John Davis Jakeman, Michael S. Eldred, Gianluca Geraci, Teresa Portone, Ahmad Rushdi, Daniel Thomas Seidl, Thomas M. Smith, (2021). Multi-fidelity Machine Learning Machine Learning and Deep Learning Conference https://www.osti.gov/search/identifier:1876608 Document ID: 1331137

Alan Hsieh, David Charles Maniaci, Thomas Herges, Gianluca Geraci, Daniel Thomas Seidl, Ryan King, (2021). Multifidelity Uncertainty Quantification for Wind Farms: The SWiFT Facility Case Study 2022 AIAA SciTech Forum and Exposition Document ID: 1318736

Alan Hsieh, David Charles Maniaci, Thomas Herges, Gianluca Geraci, Daniel Thomas Seidl, (2021). Multifidelity Uncertainty Quantification for Wind Farms: The SNL SWiFT Case Study 2022 AIAA SciTech Forum and Exposition Document ID: 1318657

Alan Hsieh, David Charles Maniaci, Gianluca Geraci, Daniel Thomas Seidl, Thomas Herges, Kenneth Brown, James Joseph Cutler, (2021). Application of Multifidelity Uncertainty Quantification Towards Multi-turbine Interaction and Wake Characterization 2021 Wind Energy Science Conference https://www.osti.gov/search/identifier:1870979 Document ID: 1317934

Timothy M. Berg, Paul Mariner, Bert Debusschere, Daniel Thomas Seidl, Rosemary Claire Leone, Kyung Won Chang, (2021). Machine Learning Surrogates for the Fuel Matrix Degradation Model Spent Fuel and Waste Science and Technology (SFWST) https://www.osti.gov/search/identifier:1869760 Document ID: 1307394

Daniel Thomas Seidl, John Davis Jakeman, (2021). Digital Twin Modeling with Gaussian Process Networks Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Document ID: 1292810

Timothy M. Berg, Kyung Won Chang, Rosemary Claire Leone, Daniel Thomas Seidl, Paul Mariner, Bert Debusschere, (2021). Surrogate Modeling of Spent Fuel Degradation for Repository Performance Assessment Society for Industrial and Applied Mathematics (SIAM) Conference on Computational Science and Engineering (CSE21) https://www.osti.gov/search/identifier:1854307 Document ID: 1280276

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, Ryan King, Hans-Joachim Bungartz, Youssef Marzouk, (2021). Multifidelity Monte Carlo Estimators for Robust Formulations in Optimization under Uncertainty Siam Cse 2021 https://www.osti.gov/search/identifier:1847580 Document ID: 1279913

Samuel Saiid Fayad, Elizabeth M. C. Jones, Phillip L. Reu, Daniel Thomas Seidl, John Lambros, (2021). Sensitivity-Based Simultaneous Experimentation and Calibration of Complex Elasto-Plastic Models Society for Experiemntal Mechanics (SEM) https://www.osti.gov/search/identifier:1847583 Document ID: 1269094

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, Hans-Joachim Bungartz, Youssef Marzouk, (2021). Multilevel Estimators for Measures of Robustness in Optimization Under Uncertainty 16th U.S. National Congress on Computational Mechanics Document ID: 1267961

Alan Hsieh, David Charles Maniaci, Gianluca Geraci, Daniel Thomas Seidl, Thomas Herges, Kenneth Brown, James Joseph Cutler, (2021). Application of Multilevel-Multifidelity Uncertainty Quantification Towards Multi-turbine Interaction and Wake Characterization Wind Energy Science Conference 2021 Document ID: 1267034

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, Ryan King, Hans-Joachim Bungartz, Youssef Marzouk, (2020). Multifidelity strategies for optimization under uncertainty of wind power plants AIAA SciTech 2021 https://www.osti.gov/search/identifier:1836901 Document ID: 1244281

Paul Mariner, Timothy M. Berg, Kyung Won Chang, Bert Debusschere, Rosemary Claire Leone, Daniel Thomas Seidl, (2020). Surrogate Model Development of Spent Fuel Degradation for Repository Performance Assessment https://www.osti.gov/search/identifier:1673178 Document ID: 1208697

Paul Mariner, Michael Anthony Nole, Eduardo Basurto, Timothy M. Berg, Kyung Won Chang, Bert Debusschere, Aubrey Celia Eckert, Mohamed Salah Ebeida, Michael Benjamin Gross, Glenn Hammond, Jacob Harvey, Spencer Holloran Jordan, Kristopher L Kuhlman, Tara LaForce, Rosemary Claire Leone, William C. McLendon, Melissa Marie Mills, Heeho Daniel Park, Frank Vinton Perry, Alex Salazar, Daniel Thomas Seidl, Sevougian David, Emily Stein, Laura Painton Swiler, (2020). Advances in GDSA Framework Development and Process Model Integration https://www.osti.gov/search/identifier:1671380 Document ID: 1208725

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Ryan King, Michael S. Eldred, Hans-Joachim Bungartz, (2020). Multifidelity Derivative-free Optimization Under Uncertainty For Wind Plants 14th World Congress on Computational Mechanics (WCCM) Document ID: 1208333

Daniel Thomas Seidl, Brian Neal Granzow, (2020). Elastoplastic Constitutive Model Calibration with Automatic Differentiation-based Sensitivities iDICs 2020 Virtual Conference https://www.osti.gov/search/identifier:1830965 Document ID: 1208250

Timothy M. Berg, Kyung Won Chang, Rosemary Claire Leone, Daniel Thomas Seidl, Paul Mariner, Bert Debusschere, (2020). Surrogate Modeling of Spent Fuel Degradation for Repository Performance Assessment SIAM Conference on Computational Science and Engineering (CSE21) Document ID: 1197226

Daniel Thomas Seidl, Brian Neal Granzow, (2020). Elastoplastic Constitutive Model Calibration with Automatic Differentiation-based Sensitivities iDICs 2020 virtual conference Document ID: 1196402

Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, Youssef Marzouk, Hans-Joachim Bungartz, (2020). Multifidelity Monte Carlo Estimators for Robust Formulations in Optimization under Uncertainty Siam Cse 2021 Document ID: 1196286

Keith Dalbey, Michael S. Eldred, Gianluca Geraci, John Davis Jakeman, Kathryn Anne Maupin, Jason A. Monschke, Daniel Thomas Seidl, Laura Painton Swiler, Anh Tran, Friedrich Menhorn, Xiaoshu Zeng, (2020). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 Theory Manual https://www.osti.gov/search/identifier:1630693 Document ID: 1127943

Brian M. Adams, William J. Bohnhoff, Keith Dalbey, Mohamed Salah Ebeida, John P. Eddy, Michael S. Eldred, Russell Hooper, Patricia D. Hough, Kenneth Hu, John Davis Jakeman, Mohammad Khalil, Kathryn Anne Maupin, Jason A. Monschke, Elliott Marshall Ridgway, Ahmad Rushdi, Daniel Thomas Seidl, John Adam Stephens, Laura Painton Swiler, Justin Winokur, (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://www.osti.gov/search/identifier:1630694 Document ID: 1127946

David Charles Maniaci, Alan Hsieh, Gianluca Geraci, Daniel Thomas Seidl, Thomas G. Herges, Michael S. Eldred, Myra L. Blaylo, Brent C Houchens, (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 FY20Q2 Report Overview https://www.osti.gov/search/identifier:1778659 Document ID: 1115975

Paul Mariner, Bert Debusschere, Glenn Edward Hammond, Daniel Thomas Seidl, Swiler Laura, Jonathon Vo, (2020). Surrogate Modeling of Spatially Heterogeneous Source Terms for Probabilistic Assessment of Repository Performance International Workshop on ?How to integrate geochemistry at affordable costs into reactive transport for large-scale systems? https://www.osti.gov/search/identifier:1763614 Document ID: 1090413

Daniel Thomas Seidl, Dakshina M. Valiveti, (2020). High-fidelity Peridynamics and Surrogate Modeling of Pressure-driven Well Stimulation Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference Document ID: 1079271

Daniel Thomas Seidl, Brian Neal Granzow, (2020). Adjoint-based Calibration of Plasticity Model Parameters from Full-field Data via Automatic Differentiation Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference Document ID: 1079273

Alan Hsieh, David Charles Maniaci, Thomas Herges, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, Lawrence Cheung, Myra L. Blaylock, Brent C Houchens, (2020). Multilevel-Multifidelity Uncertainty Quantification Using Dakota of Atmospheric Boundary Layers Under Different Stability Regimes 14th World Congress in Computational Mechanics (WCCM) ECCOMAS Congress 2020 Document ID: 1079551

Daniel Thomas Seidl, Brian Neal Granzow, (2020). Calibration of Plasticity Model Parameters from Full-field Data with Automatic Differentiation-based Sensitivities 14th WCCM and ECCOMAS Congress 2020 Document ID: 1078848

Alan Hsieh, David Charles Maniaci, Thomas Herges, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, Myra L. Blaylock, Brent C Houchens, (2020). Multilevel Uncertainty Quantification Using CFD and OpenFAST Simulations of the SWiFT Facility AIAA SciTech 2020 https://www.osti.gov/search/identifier:1760989 Document ID: 1079056

Menhorn Friedrich, Gianluca Geraci, Daniel Thomas Seidl, Michael S. Eldred, King Ryan, Bungartz Hans-Joachim, Marzouk Youssef, (2020). Higher moment multilevel estimators for optimization under uncertainty applied to wind plant design AIAA Scitech 2020 https://www.osti.gov/search/identifier:1643359 Document ID: 1078776

Paul Mariner, Bert Debusschere, Glenn Edward Hammond, Daniel Thomas Seidl, Laura Swiler, Jonathan Vo, (2019). Surrogate Modeling of Spatially Heterogeneous Source Terms for Probabilistic Assessment of Repository Performance International Workshop on How to integrate geochemistry at affordable costs into reactive transport for large-scale systems Document ID: 1066749

Daniel Thomas Seidl, Dawei Song, Assad Oberai, (2019). Three-dimensional traction microscopy accounting for cell-induced matrix alteration Preprint Server / Computer Methods in Applied Mechanics and Engineering https://www.osti.gov/search/identifier:1618078 Document ID: 1055888

Samuel Saiid Fayad, Daniel Thomas Seidl, Phillip L. Reu, (2019). Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization Experimental Mechanics https://www.osti.gov/search/identifier:1574475 Document ID: 1032327

Paul Mariner, Bert Debusschere, James Jerden, Daniel Thomas Seidl, Laura Painton Swiler, Jonathan Vo, (2019). Lessons Learned in the Development of Source Term Surrogate Models for Repository Performance Assessment DECOVALEX 2019 Symposium https://www.osti.gov/search/identifier:1643084 Document ID: 1054987

Samuel Saiid Fayad, Daniel Thomas Seidl, Phillip L. Reu, (2019). Minimizing Pattern Induced Bias in Digital Image Correlation iDICS 2019 https://www.osti.gov/search/identifier:1642830 Document ID: 1033296

Paul Mariner, Laura A. Connolly, Leigh Cunningham, Bert Debusschere, David Charles Dobson, Jennifer M Frederick, Glenn Edward Hammond, Spencer Holloran Jordan, Tara LaForce, Michael Anthony Nole, Heeho Daniel Park, Frank Vinton Perry, Ralph David Rogers, Daniel Thomas Seidl, Stephen David Sevougian, Emily Stein, Peter N. Swift, Laura Painton Swiler, Jonathan Vo, Michael Gary Wallace, (2019). Progress in Deep Geologic Disposal Safety Assessment in the U.S. since 2010 https://www.osti.gov/search/identifier:1570094 Document ID: 1032089

Michael S. Eldred, Gianluca Geraci, Daniel Thomas Seidl, Friedrich Menhorn, Ryan King, Thomas Herges, Alan Hsieh, David Charles Maniaci, (2019). Milestone: Develop multilevel emulator-based Bayesian inference capabilities and demonstrate data assimilation for SWiFT configuration A2e FY19 milestone completion evidence https://www.osti.gov/search/identifier:1646013 Document ID: 1032321

Daniel Thomas Seidl, Assad A. Oberai, Paul E. Barbone, (2019). The Coupled Adjoint-State Equation in Forward and Inverse Linear Elasticity: Incompressible Plane Stress Computer Methods in Applied Mechanics and Engineering https://www.osti.gov/search/identifier:1559519 Document ID: 998834

Samuel (SNL) Fayad, Phillip (SNL) Reu, Daniel Thomas Seidl, (2019). Minimizing Pattern Induced Bias in Digital Image Correlation iDICS 2019 Document ID: 985255

Paul Mariner, Laura Painton Swiler, Daniel Thomas Seidl, Jonathan Vo, (2019). Lessons Learned in the Development of Source Term Surrogate Models for Repository Performance Assessment DECOVALEX 2019 Symposium https://www.osti.gov/search/identifier:1640670 Document ID: 973411

Samuel Saiid Fayad, Phillip L. Reu, Daniel Thomas Seidl, (2019). Pattern Induced Bias in Digital Image Correlation Society of Experimental Mechanics https://www.osti.gov/search/identifier:1640639 Document ID: 972798

Paul Mariner, Daniel Thomas Seidl, Laura Painton Swiler, Bert Debusschere, Jonathan Vo, Jim Jerden, Jennifer M Frederick, (2019). Surrogate Modeling of Fuel Dissolution Spent Fuel and Waste Disposition (SFWD) Annual Working Group Meeting https://www.osti.gov/search/identifier:1648825 Document ID: 971786

Daniel Thomas Seidl, Bart G van Bloemen Waanders, Timothy Michael Wildey, (2019). Simultaneous Inversion of Shear Modulus and Traction Boundary Conditions in Biomechanical Imaging Inverse Problems in Science and Engineering https://www.osti.gov/search/identifier:1515211 Document ID: 948096

Paul Mariner, Laura Painton Swiler, Daniel Thomas Seidl, Bert Debusschere, Johnathan Vo, Jennifer M Frederick, (2019). High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance 2019 International High Level Radioactive Waste Management Conference https://www.osti.gov/search/identifier:1639277 Document ID: 936065

Paul Mariner, Laura Painton Swiler, Daniel Thomas Seidl, Bert Debusschere, Johnathan Vo, Jennifer M Frederick, (2019). High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance 2019 International High Level Radioactive Waste Management Conference https://www.osti.gov/search/identifier:1602117 Document ID: 924977

Paul Mariner, Laura P. Swiler, Daniel Thomas Seidl, Bert J. Debusschere, Jonathan Vo, Jennifer M Frederick, James L. Jerden, (2019). High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance 2019 International High Level Radioactive Waste Management Conference https://www.osti.gov/search/identifier:1595546 Document ID: 912974

Daniel Thomas Seidl, Brian Neal Granzow, (2018). Adjoint-based Calibration of Plasticity Model Parameters from Full-field Data Annual Conference and Exposition on Experimental and Applied Mechanics Document ID: 888802

Daniel Thomas Seidl, Bart G van Bloemen Waanders, Timothy Michael Wildey, (2018). Simultaneous Inversion of Heterogeneous Traction Boundary Conditions and Shear Modulus in Soft Biomaterials Annual Conference and Exposition on Experimental and Applied Mechanics Document ID: 888804

R. Allen Roach, Nicolas Argibay, Kyle Matthew Allen, Dorian K. Balch, Lauren L. Beghini, Joseph E. Bishop, Brad Boyce, Judith Alice Brown, Ross L. Burchard, Michael E. Chandross, Adam Cook, Christopher DiAntonio, Amber Dawn Dressler, Eric Christopher Forrest, Kurtis Ross Ford, Thomas Ivanoff, Bradley Howell Jared, Kyle Johnson, Daniel Kammler, Joshua Robert Koepke, Andrew Kustas, Judith Maria Lavin, Nicholas Leathe, Brian T. Lester, Jonathan D Madison, Seethambal S. Mani, Mario J. Martinez, Daniel Moser, Theron Rodgers, Daniel Thomas Seidl, Harlan James Brown-Shaklee, Joshua Stanford, Michael Stender, Joshua Daniel Sugar, Laura Painton Swiler, Samantha Taylor, Bradley Trembacki, (2018). Born Qualified Grand Challenge LDRD Final Report https://www.osti.gov/search/identifier:1481619 Document ID: 875668

Elizabeth M. C. Jones, Jay D. Carroll, Kyle N. Karlson, Sharlotte LorraineBolyard Kramer, Richard B. Lehoucq, Phillip L. Reu, Daniel Thomas Seidl, Daniel Z. Turner, (2018). High-throughput Material Characterization using the Virtual Fields Method https://www.osti.gov/search/identifier:1474817 Document ID: 854985

Brian Neal Granzow, Daniel Thomas Seidl, (2018). Adjoint-based Calibration of Plasticity Model Parameters from Digital Image Correlation Data https://www.osti.gov/search/identifier:1474264 Document ID: 865611

Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Daniel Thomas Seidl, Bart G van Bloemen Waanders, (2018). Data-informed Multiscale Modeling of Additive Materials Engineering Mechanics Institute Conference 2018 https://www.osti.gov/search/identifier:1523778 Document ID: 807805

Daniel Thomas Seidl, Bart G van Bloemen Waanders, Timothy Michael Wildey, (2018). Multiscale Interfaces for Large Scale Optimization SIAM Conference on Uncertainty Quantification https://www.osti.gov/search/identifier:1525680 Document ID: 784306

Daniel Thomas Seidl, Daniel Z. Turner, Elizabeth M. C. Jones, Kyle N. Karlson, Phillip L. Reu, (2018). Optimal Mechanical Testing for Constitutive Parameter Identification 2018 Society for Experimental Mechanics Conference and Exposition https://www.osti.gov/search/identifier:1498447 Document ID: 771353

R. Allen Roach, Bradley Howell Jared, Adam Cook, David M Keicher, Bart G van Bloemen Waanders, Laura Painton Swiler, Daniel Thomas Seidl, Timothy Michael Wildey, Shaun R Whetten, (2018). Born Qualified EAB Telecon BQ EAB Telecon https://www.osti.gov/search/identifier:1514821 Document ID: 739323

Daniel Thomas Seidl, Daniel Z. Turner, Elizabeth M. C. Jones, Kyle N. Karlson, Phillip L. Reu, (2017). Optimal Mechanical Testing for Constitutive Parameter Identification 2018 Society for Experimental Mechanics Conference and Exposition Document ID: 725123

Daniel Thomas Seidl, Paul (BU) Barbone, Assad (RPI) Oberai, Timothy Michael Wildey, Bart G van Bloemen Waanders, (2017). PDE-Constrained Optimization for Heterogeneous Mechanical Property Estimation of Biomaterials International Digital Image Correlation Society 2017 Conference & Workshop Document ID: 671953

Timothy Michael Wildey, Bart G van Bloemen Waanders, Daniel Thomas Seidl, (2017). Adaptive Multiscale Modeling Using Generalized Mortar Methods US National Congress on Computational Mechanics https://www.osti.gov/search/identifier:1513505 Document ID: 637802

Timothy Michael Wildey, Bart G van Bloemen Waanders, Daniel Thomas Seidl, (2017). Multiscale Modeling Using Mortar Methods Engineering Mechanics Institute Conference https://www.osti.gov/search/identifier:1458195 Document ID: 625646

Daniel Thomas Seidl, Bart G van Bloemen Waanders, Timothy Michael Wildey, (2017). Multiscale Interfaces for Large Scale Optimization Optimization Under Uncertainty and Data-Driven Science and Engineering Workshop https://www.osti.gov/search/identifier:1456522 Document ID: 612151

Bart G van Bloemen Waanders, Timothy Michael Wildey, Daniel Thomas Seidl, Harriet Li, (2017). Multiscale optimization under uncertainty for additive manufacturing SIAM Conference on Computational Science and Engineering https://www.osti.gov/search/identifier:1426379 Document ID: 599475

Bart G van Bloemen Waanders, Timothy Michael Wildey, Daniel Thomas Seidl, Harriet Li, (2017). Multiscale optimization under uncertainty Pioneer Natural Resources, Oak Ridge National Laboratories, and Sandia National Laboratories Workshop https://www.osti.gov/search/identifier:1458249 Document ID: 589318

Daniel Thomas Seidl, Bart G van Bloemen Waanders, Timothy Michael Wildey, (2017). Simultaneous Estimation of Material Parameters and Neumann Boundary Conditions in a Linear Elastic Model by PDE-Constrained Optimization Siam Cse 2017 https://www.osti.gov/search/identifier:1458297 Document ID: 577926

Daniel Thomas Seidl, Paul E. (Boston University) Barbone, Assad A. (RPI) Oberai, (2016). The Coupled-Adjoint State Equation in Forward and Inverse Elasticity International Tissue Elasticity Conference Document ID: 475923

Timothy Michael Wildey, Bart G van Bloemen Waanders, Daniel Thomas Seidl, (2016). Uncertainty Quantification for Multiscale Mortar Methods Probabilistic Mechanics and Reliability Conference https://www.osti.gov/search/identifier:1368791 Document ID: 453711

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