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Publication	Type	Year
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
A machine learning surrogate for density functional theory based on the local density of state 2022 Workshop on Recent Developments in Electronic Structure (ES22) Normand A. Modine, Lenz Fiedler, Dayton Jonathan Vogel, Aidan P. Thompson, Austin Ellis, John Adam Stephens, Gabe Popoola, Attila Cangi, Sivasankaran Rajamanickam	Conference Presentation – 2022 Conference Presentation	2022
Flexible CI/CD Software Tools for the Dakota project Tri-lab Advanced Simulation & Computing Sustainable Scientific Software Conference John Adam Stephens, Brian M. Adams, Wesley Poblete Coomber, Elliott Marshall Ridgway	Conference Presentation – 2022 Conference Presentation	2022
A machine learning surrogate for density functional theory based on the local density of states 2022 Workshop on Recent Developments in Electronic Structure (ES22) Normand A. Modine, Lenz Fiedler, Dayton Jonathan Vogel, Aidan P. Thompson, Austin Ellis, John Adam Stephens, Gabe Popoola, Attila Cangi, Sivasankaran Rajamanickam	Abstract – 2022 Abstract	2022
Flexible software tools for CI/CD for the Dakota project 2022 Tri-lab Advanced Simulation & Computing Sustainable Scientific Software Conference John Adam Stephens, Brian M. Adams, Wesley Poblete Coomber, Elliott Marshall Ridgway	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
Dakota Software For Optimization, Uncertainty Quantification And Model Calibration Eccomas 2022 Daniel Thomas Seidl, Brian M. Adams, John Adam Stephens, Gianluca Geraci	Abstract – 2021 Abstract	2021
Developing uncertainty quantification strategies in electromagnetic problems involving highly resonant cavities Journal of Verification, Validation and Uncertainty Quantification Salvatore Campione, John Adam Stephens, Nevin Martin, Aubrey Celia Eckert, Larry K. Warne, Jose Gabriel Huerta, Robert Anthony Pfeiffer, Adam Jones https://www.osti.gov/search/identifier:1822223	Journal Article – 2021 Journal Article	2021
Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks Austin Ellis, Lenz Fielder, Gabriel ANUOLUWAPO Popoola, Normand A. Modine, John Adam Stephens, Aidan P. Thompson, Sivasankaran Rajamanickam https://www.osti.gov/search/identifier:1817970	Report – 2021 Report	2021
Dakota: Uncertainty Quantification and Beyond IAEA EVT2005676 (Virtual Event) Consultancy Meeting on MELCOR/DAKOTA Coupling in SNAP and Stand-Alone Severe Accident and Uncertainty Analysis Brian M. Adams, John Adam Stephens https://www.osti.gov/search/identifier:1856466	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Accelerating Multiscale Materials Modeling with Machine Learning SIAM Conference on Computational Science an Engineering J. Austin Ellis, Sivasankaran Rajamanickam, Normand A. Modine, Aidan P. Thompson, John Adam Stephens, Attila Cangi https://www.osti.gov/search/identifier:1853873	Conference Presentation – 2021 Conference Presentation	2021
Uncertainty Quantification In Electromagnetic Wccm-eccomas Adam Jones, Salvatore Campione, John Adam Stephens, Aubrey Celia Eckert, Larry K. Warne, Jose Gabriel Huerta, Robert Anthony Pfeiffer https://www.osti.gov/search/identifier:1837143	Conference Presentation – 2020 Conference Presentation	2020
Accelerating Finite-temperature Kohn-Sham Density Functional Theory\ with Deep Neural Networks John Ellis, Attila cangi, Normand A. Modine, John Adam Stephens, Aidan P. Thompson, Sivasankaran Rajamanickam https://www.osti.gov/search/identifier:1677521	Report – 2020 Report	2020
Managing execution of Dakota evaluations with Parsl Parslfest 2020 John Adam Stephens https://www.osti.gov/search/identifier:1824264	Conference Presentation – 2020 Conference Presentation	2020
Dakota: New and Emerging Features DoD-SNL ModSim Workshop John Adam Stephens	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User?s Manual 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 https://www.osti.gov/search/identifier:1630694	SAND Report – 2020 SAND Report	2020
Dakota: What's New? 14th WCCM and ECCOMAS Congress 2020 John Adam Stephens	Abstract – 2020 Abstract	2020
Uncertainty Quantification In Electromagnetic Problems Of Highly Resonant Cavities Using Dakota 14th World Congress in Computational Mechanics and ECCOMAS Congress Salvatore Campione, John Adam Stephens, Nevin Martin, Aubrey Celia Eckert, Larry K. Warne, Jose Gabriel Huerta, Robert Anthony Pfeiffer, Adam Jones https://www.osti.gov/search/identifier:1761295	Conference Paper – 2020 Conference Paper	2020
Redesign of a forging geometry using Dakota optimization software 15th US National Congress on Computational Mechanics Lauren L. Beghini, Andrew Joseph Stershic, John Adam Stephens https://www.osti.gov/search/identifier:1641383	Conference Paper – 2019 Conference Paper	2019
Dakota: What's New? US National Congress on Computational Mechanics John Adam Stephens, Brian M. Adams, Gianluca Geraci https://www.osti.gov/search/identifier:1641381	Conference Paper – 2019 Conference Paper	2019
Dakota and Sierra Tools for Uncertainty Quantification and Advanced Modeling and Simulation SNL Dakota & Sierra Seminar Matthew R Denman, John Adam Stephens, Aubrey Celia Eckert, Martin W. Heinstein	Presentation (non-conference) – 2018 Presentation (non-conference)	2018
Dakota Optimization and UQ: Explore and Predict with Confidence 2018 SIAM Conference on Uncertainty Quantification Brian M. Adams, Patricia D. Hough, John Adam Stephens https://www.osti.gov/search/identifier:1513478	Conference Paper – 2018 Conference Paper	2018
Dakota Optimization and UQ: Explore and Predict with Confidence Invited Presentation at LANL Brian M. Adams, John Adam Stephens https://www.osti.gov/search/identifier:1507940	Presentation (non-conference) – 2018 Presentation (non-conference)	2018
Dakota: Explore and Predict with Confidence DoD/SNL Workshop John Adam Stephens, Brian M. Adams https://www.osti.gov/search/identifier:1525600	Conference Paper – 2018 Conference Paper	2018
UQ Theories, Principles, and Tools: Dakota Topics Quantification of Uncertainty Workshop Laura Painton Swiler, John Adam Stephens https://www.osti.gov/search/identifier:1463778	Conference Paper – 2017 Conference Paper	2017
Document Title	Type	Year

Results 1–25 of 36

Publications

Overview of the latest features and capabilities in the Dakota software

A machine learning surrogate for density functional theory based on the local density of state

Flexible CI/CD Software Tools for the Dakota project

A machine learning surrogate for density functional theory based on the local density of states

Flexible software tools for CI/CD for the Dakota project

Overview Of The Latest Features And Capabilities In The Dakota Software

Dakota Software For Optimization, Uncertainty Quantification And Model Calibration

Developing uncertainty quantification strategies in electromagnetic problems involving highly resonant cavities

Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

Dakota: Uncertainty Quantification and Beyond

Accelerating Multiscale Materials Modeling with Machine Learning

Uncertainty Quantification In Electromagnetic

Accelerating Finite-temperature Kohn-Sham Density Functional Theory\ with Deep Neural Networks

Managing execution of Dakota evaluations with Parsl

Dakota: New and Emerging Features

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User?s Manual

Dakota: What's New?

Uncertainty Quantification In Electromagnetic Problems Of Highly Resonant Cavities Using Dakota

Redesign of a forging geometry using Dakota optimization software

Dakota: What's New?

Dakota and Sierra Tools for Uncertainty Quantification and Advanced Modeling and Simulation

Dakota Optimization and UQ: Explore and Predict with Confidence

Dakota Optimization and UQ: Explore and Predict with Confidence

Dakota: Explore and Predict with Confidence

UQ Theories, Principles, and Tools: Dakota Topics