Publications

Advanced Search

Results 1–25 of 102

Scope

Center for Computing Research

All Sandia Publications

Publication Type

Author

Funding Source

Current Filters Clear all

Remove author filter×

Publication	Type	Year
Reduction of Full-Field Data by Spectral Decomposition International Digital Image Correlation Society Conference Denielle Ricciardi, Daniel Thomas Seidl, Brian T. Lester, Amanda Jones, Matthew Wilson Kury, Elizabeth M. C. Jones	Abstract – 2022 Abstract	2022
Direct-Levelling Finite Element Analysis Data for Material Model Calibration using Digital Image Correlation and Finite Element Model Updating IDICs Annual Conference Samuel Saiid Fayad, Elizabeth M. C. Jones, Daniel Thomas Seidl, Phillip L. Reu, John Lambros	Abstract – 2022 Abstract	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 Daniel Thomas Seidl, Brian Neal Granzow	Abstract – 2022 Abstract	2022
Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration Tms Denielle Ricciardi, Daniel Thomas Seidl, Brian T. Lester, Amanda Jones, Elizabeth M. C. Jones	Abstract – 2022 Abstract	2022
Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository International High-Level Radioactive Waste Management Conference Bert Debusschere, Daniel Thomas Seidl, Timothy M. Berg, Kyung Won Chang, Rosemary Claire Leone, Laura Painton Swiler, Paul Mariner	Conference Paper – 2022 Conference Paper	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 Daniel Thomas Seidl, Brian Neal Granzow	Conference Presentation – 2022 Conference Presentation	2022
Finite Element Model Levelling for Material Model Calibration using Digital Image Correlation Society for Experimental Mechanics Annual Conference Samuel Saiid Fayad, Daniel Thomas Seidl, Phillip L. Reu, Elizabeth M. C. Jones, John Lambros	Conference Presentation – 2022 Conference Presentation	2022
Application of Forward Multifidelity Uncertainty Quantification to Wind Farms AIAA SciTech 2023 Alan Hsieh, David Charles Maniaci, Thomas Herges, Gianluca Geraci, Daniel Thomas Seidl	Abstract – 2022 Abstract	2022
Multilevel Monte Carlo derivative-free optimization under uncertainty of wind power plants 8th European Congress on Computational Methods in Applied Sciences and Engineering Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Ryan King, Michael S. Eldred, Hans-Joachim Bungartz, Youssef Marzouk	Conference Presentation – 2022 Conference Presentation	2022
Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository DOE Computational Research Leadership Council (CRLC) Seminar Series Bert Debusschere, Timothy M. Berg, Daniel Thomas Seidl, Kyung Won Chang, Rosemary Claire Leone, Laura Painton Swiler, Paul Mariner	Abstract – 2022 Abstract	2022
Interlaced Characterization and Calibration: Oline Bayesian Optimal Experimental Design for Constitutive Model Calibration AIAA SciTech Denielle Ricciardi, Daniel Thomas Seidl, Brian T. Lester, Amanda Jones, Elizabeth M. C. Jones	Abstract – 2022 Abstract	2022
Overview of the latest features and capabilities in the Dakota software 2022 ECCOMAS Congress John Adam Stephens, Daniel Thomas Seidl, Brian M. Adams, Gianluca Geraci	Conference Presentation – 2022 Conference Presentation	2022
Improving Digital Twins by Learning from a Fleet of Assets SIAM Conference on Uncertainty Quantification Daniel Thomas Seidl, John Davis Jakeman	Conference Presentation – 2022 Conference Presentation	2022
Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty Siam Uq 22 Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Youssef Marzouk, Michael S. Eldred, Hans-Joachim Bungartz	Conference Presentation – 2022 Conference Presentation	2022
Peridynamics and Surrogate Modeling of Pressure-driven Well Stimulation International Journal of Rock Mechanics and Mining Sciences Daniel Thomas Seidl, Dakshina M. Valiveti https://www.osti.gov/search/identifier:1870476	Journal Article – 2022 Journal Article	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 Laura Painton Swiler, Teresa Portone, Paul Mariner, Rosie Leone, Dusty Marie Brooks, Daniel Thomas Seidl, Bert Debusschere, Tim Berg	Conference Presentation – 2022 Conference Presentation	2022
Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration SIAM Conference on Mathematics of Data Science Daniel Thomas Seidl, Brian T. Lester, Elizabeth M. C. Jones, Denielle Ricciardi, Amanda Jones	Abstract – 2022 Abstract	2022
Interlaced Characterization and Calibration of Elastoplastic Constitutive Models SIAM Conference on the Mathematics of Data Science Daniel Thomas Seidl, Denielle Ricciardi, Brian T. Lester, Amanda Jones, Elizabeth M. C. Jones	Abstract – 2022 Abstract	2022
Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository 2022 International High Level Radioactive Waste Management Conference Bert Debusschere, Timothy M. Berg, Daniel Thomas Seidl, Kyung Won Chang, Rosemary Claire Leone, Laura Painton Swiler, Paul Mariner	Abstract – 2022 Abstract	2022
Levelling of Finite Element Models for Material Model Calibration using Digital Image Correlation Society for Experimental Mechanics Annual Conference Samuel Saiid Fayad, Elizabeth M. C. Jones, Phillip L. Reu, Daniel Thomas Seidl, John Lambros	Conference Proceeding – 2022 Conference Proceeding	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 Daniel Thomas Seidl, Brian Neal Granzow	Abstract – 2022 Abstract	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) Friedrich Menhorn, Gianluca Geraci, Daniel Thomas Seidl, Ryan King, Michael S. Eldred, Hans-Joachim Bungartz, Youssef Marzouk	Abstract – 2022 Abstract	2022
Overview Of The Latest Features And Capabilities In The Dakota Software 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) John Adam Stephens, Daniel Thomas Seidl, Brian M. Adams, Gianluca Geraci	Abstract – 2021 Abstract	2021
Calibration of Elastoplastic Constitutive Model Parameters from Full-field Data with Automatic Differentiation-based Sensitivities International Journal for Numerical Methods in Engineering Daniel Thomas Seidl, Brian Neal Granzow https://www.osti.gov/search/identifier:1828784	Journal Article – 2021 Journal Article	2021
Improving Digital Twins by Learning from a Fleet of Assets SIAM Conference on Uncertainty Quantification (UQ22) Daniel Thomas Seidl, John Davis Jakeman	Abstract – 2021 Abstract	2021
Document Title	Type	Year

Results 1–25 of 102

Publications

Reduction of Full-Field Data by Spectral Decomposition

Direct-Levelling Finite Element Analysis Data for Material Model Calibration using Digital Image Correlation and Finite Element Model Updating

Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data

Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration

Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository

Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data

Finite Element Model Levelling for Material Model Calibration using Digital Image Correlation

Application of Forward Multifidelity Uncertainty Quantification to Wind Farms

Multilevel Monte Carlo derivative-free optimization under uncertainty of wind power plants

Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository

Interlaced Characterization and Calibration: Oline Bayesian Optimal Experimental Design for Constitutive Model Calibration

Overview of the latest features and capabilities in the Dakota software

Improving Digital Twins by Learning from a Fleet of Assets

Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty

Peridynamics and Surrogate Modeling of Pressure-driven Well Stimulation

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

Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration

Interlaced Characterization and Calibration of Elastoplastic Constitutive Models

Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository

Levelling of Finite Element Models for Material Model Calibration using Digital Image Correlation

Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data

Multilevel Monte Carlo derivative-free optimization under uncertainty of wind power plants

Overview Of The Latest Features And Capabilities In The Dakota Software

Calibration of Elastoplastic Constitutive Model Parameters from Full-field Data with Automatic Differentiation-based Sensitivities

Improving Digital Twins by Learning from a Fleet of Assets