John Adam Stephens

Optimization & Uncertainty Quantification

Author profile picture

Optimization & Uncertainty Quantification

jasteph@sandia.gov

(505) 844-0407

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1318

Biography

Adam joined Sandia in 2013 as a senior member of technical staff. He currently works primarily as a developer of Dakota (https://dakota.sandia.gov) and leads the Dakota Mission Integration thrust, which is charged with bridging the gap between upstream R&D and ordinary Dakota users. Adam’s research background and interests are in atomistic modeling and heterogeneous catalysis.

Education

  • B.S., Chemical Engineering, Texas Tech University (2002)
  • Ph.D., Chemical Engineering, The University of Texas at Austin (2012)

Publications

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

Normand A. Modine, Lenz Fiedler, Dayton Jonathan Vogel, Aidan P. Thompson, Austin Ellis, John Adam Stephens, Gabe Popoola, Attila Cangi, Sivasankaran Rajamanickam, (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) Document ID: 1539599

John Adam Stephens, Brian M. Adams, Wesley Poblete Coomber, Elliott Marshall Ridgway, (2022). Flexible CI/CD Software Tools for the Dakota project Tri-lab Advanced Simulation & Computing Sustainable Scientific Software Conference Document ID: 1539078

Normand A. Modine, Lenz Fiedler, Dayton Jonathan Vogel, Aidan P. Thompson, Austin Ellis, John Adam Stephens, Gabe Popoola, Attila Cangi, Sivasankaran Rajamanickam, (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) Document ID: 1528847

John Adam Stephens, Brian M. Adams, Wesley Poblete Coomber, Elliott Marshall Ridgway, (2022). Flexible software tools for CI/CD for the Dakota project 2022 Tri-lab Advanced Simulation & Computing Sustainable Scientific Software Conference Document ID: 1494340

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

Salvatore Campione, John Adam Stephens, Nevin Martin, Aubrey Celia Eckert, Larry K. Warne, Jose Gabriel Huerta, Robert Anthony Pfeiffer, Adam Jones, (2021). Developing uncertainty quantification strategies in electromagnetic problems involving highly resonant cavities Journal of Verification, Validation and Uncertainty Quantification https://www.osti.gov/search/identifier:1822223 Document ID: 1330574

Austin Ellis, Lenz Fielder, Gabriel ANUOLUWAPO Popoola, Normand A. Modine, John Adam Stephens, Aidan P. Thompson, Sivasankaran Rajamanickam, (2021). Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks https://www.osti.gov/search/identifier:1817970 Document ID: 1329636

Brian M. Adams, John Adam Stephens, (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 https://www.osti.gov/search/identifier:1856466 Document ID: 1281517

J. Austin Ellis, Sivasankaran Rajamanickam, Normand A. Modine, Aidan P. Thompson, John Adam Stephens, Attila Cangi, (2021). Accelerating Multiscale Materials Modeling with Machine Learning SIAM Conference on Computational Science an Engineering https://www.osti.gov/search/identifier:1853873 Document ID: 1280043

Adam Jones, Salvatore Campione, John Adam Stephens, Aubrey Celia Eckert, Larry K. Warne, Jose Gabriel Huerta, Robert Anthony Pfeiffer, (2020). Uncertainty Quantification In Electromagnetic Wccm-eccomas https://www.osti.gov/search/identifier:1837143 Document ID: 1243969

John Ellis, Attila cangi, Normand A. Modine, John Adam Stephens, Aidan P. Thompson, Sivasankaran Rajamanickam, (2020). Accelerating Finite-temperature Kohn-Sham Density Functional Theory\ with Deep Neural Networks https://www.osti.gov/search/identifier:1677521 Document ID: 1208954

John Adam Stephens, (2020). Managing execution of Dakota evaluations with Parsl Parslfest 2020 https://www.osti.gov/search/identifier:1824264 Document ID: 1208228

John Adam Stephens, (2020). Dakota: New and Emerging Features DoD-SNL ModSim Workshop Document ID: 1150837

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

John Adam Stephens, (2020). Dakota: What’s New? 14th WCCM and ECCOMAS Congress 2020 Document ID: 1079498

Salvatore Campione, John Adam Stephens, Nevin Martin, Aubrey Celia Eckert, Larry K. Warne, Jose Gabriel Huerta, Robert Anthony Pfeiffer, Adam Jones, (2020). Uncertainty Quantification In Electromagnetic Problems Of Highly Resonant Cavities Using Dakota 14th World Congress in Computational Mechanics and ECCOMAS Congress https://www.osti.gov/search/identifier:1761295 Document ID: 1079303

Lauren L. Beghini, Andrew Joseph Stershic, John Adam Stephens, (2019). Redesign of a forging geometry using Dakota optimization software 15th US National Congress on Computational Mechanics https://www.osti.gov/search/identifier:1641383 Document ID: 996749

John Adam Stephens, Brian M. Adams, Gianluca Geraci, (2019). Dakota: What’s New? US National Congress on Computational Mechanics https://www.osti.gov/search/identifier:1641381 Document ID: 996847

Matthew R Denman, John Adam Stephens, Aubrey Celia Eckert, Martin W. Heinstein, (2018). Dakota and Sierra Tools for Uncertainty Quantification and Advanced Modeling and Simulation SNL Dakota & Sierra Seminar Document ID: 842653

Brian M. Adams, Patricia D. Hough, John Adam Stephens, (2018). Dakota Optimization and UQ: Explore and Predict with Confidence 2018 SIAM Conference on Uncertainty Quantification https://www.osti.gov/search/identifier:1513478 Document ID: 784851

Brian M. Adams, John Adam Stephens, (2018). Dakota Optimization and UQ: Explore and Predict with Confidence Invited Presentation at LANL https://www.osti.gov/search/identifier:1507940 Document ID: 738845

John Adam Stephens, Brian M. Adams, (2018). Dakota: Explore and Predict with Confidence DoD/SNL Workshop https://www.osti.gov/search/identifier:1525600 Document ID: 783657

Laura Painton Swiler, John Adam Stephens, (2017). UQ Theories, Principles, and Tools: Dakota Topics Quantification of Uncertainty Workshop https://www.osti.gov/search/identifier:1463778 Document ID: 658998

John Adam Stephens, (2017). Algorithms for Design Exploration and Simulation Credibility SIAM CS&E https://www.osti.gov/search/identifier:1424866 Document ID: 589422

John Adam Stephens, (2016). Dakota: Algorithms for Design Exploration and Simulation Credibility SIAM CS&E Document ID: 529707

Elizabeth Decolvenaere, Aidan P. Thompson, John Adam Stephens, (2016). SNAP Update 2016 Annual Review https://www.osti.gov/search/identifier:1399191 Document ID: 528954

John Adam Stephens, (2016). Dakota: From Research to Production 2016 Cis Erb Document ID: 453750

Brian M. Adams, John Adam Stephens, (2016). Dakota: Algorithms for Design Exploration and Simulation Credibility; Data Analysis Capabilities Conference on Data Analysis https://www.osti.gov/search/identifier:1346113 Document ID: 409180

Patricia D. Hough, John Adam Stephens, (2016). Dakota: Algorithms for Design Exploration and Simulation Credibility Scientific Software Days https://www.osti.gov/search/identifier:1344843 Document ID: 408532

Brian M. Adams, Patricia D. Hough, John Adam Stephens, (2016). Dakota Software Training: Uncertainty Quantification Dakota Software Training https://www.osti.gov/search/identifier:1239352 Document ID: 398041

Keith Dalbey, Brian M. Adams, John Adam Stephens, Laura Painton Swiler, (2015). Dakota Sensitivity Analysis and Uncertainty Quantification, with Examples CDSE Days https://www.osti.gov/search/identifier:1244466 Document ID: 232873

Aidan P. Thompson, Peter Andrew Schultz, Paul Crozier, Stan Gerald Moore, Laura Painton Swiler, John Adam Stephens, Christian Robert Trott, Stephen M. Foiles, Garritt J. (Drexel University) Tucker, (2014). Automated Algorithms for Quantum-Level Accuracy in Atomistic Simulations: LDRD Final Report https://www.osti.gov/search/identifier:1158668 Document ID: 155463

Brian M. Adams, Lara E Bauman, William J. Bohnhoff, Keith Dalbey, John P. Eddy, Mohamed Salah Ebeida, Michael S. Eldred, Patricia D. Hough, Kenneth Hu, John Davis Jakeman, Laura Painton Swiler, John Adam Stephens, Dena Vigil, Timothy Michael Wildey, (2014). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 6.0 Users ManualVersion 6.0 Users Manual https://www.osti.gov/search/identifier:1177077 Document ID: 5336051

Brian M. Adams, Lara E Bauman, William J. Bohnhoff, Keith Dalbey, John P. Eddy, Mohamed Salah Ebeida, Michael S. Eldred, Patricia D. Hough, Kenneth Hu, John Davis Jakeman, Laura Painton Swiler, John Adam Stephens, Dena Vigil, Timothy Michael Wildey, (2014). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 6.0 Theory ManualVersion 6.0 Theory Manual https://www.osti.gov/search/identifier:1177048 Document ID: 5336050

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