Publications

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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