Publications

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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

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

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

GDSA Framework Development and Process Model Integration FY2021

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

https://www.osti.gov/search/identifier:1825056

Report – 2021 Report 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)

Laura Painton Swiler, Teresa Portone, Paul Mariner, Rosemary Claire Leone, Dusty Marie Brooks, Daniel Thomas Seidl, Bert Debusschere, Timothy M. Berg

Abstract – 2021 Abstract 2021

Machine Learning Surrogates for the Fuel Matrix Degradation Model

Spent Fuel and Waste Science and Technology (SFWST)

Timothy M. Berg, Paul Mariner, Bert Debusschere, Daniel Thomas Seidl, Rosemary Claire Leone, Kyung Won Chang

https://www.osti.gov/search/identifier:1869760

Presentation (non-conference) – 2021 Presentation (non-conference) 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)

Timothy M. Berg, Kyung Won Chang, Rosemary Claire Leone, Daniel Thomas Seidl, Paul Mariner, Bert Debusschere

https://www.osti.gov/search/identifier:1854307

Conference Presentation – 2021 Conference Presentation 2021

Surrogate Model Development of Spent Fuel Degradation for Repository Performance Assessment

Paul Mariner, Timothy M. Berg, Kyung Won Chang, Bert Debusschere, Rosemary Claire Leone, Daniel Thomas Seidl

https://www.osti.gov/search/identifier:1673178

Report – 2020 Report 2020

Advances in GDSA Framework Development and Process Model Integration

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

https://www.osti.gov/search/identifier:1671380

Report – 2020 Report 2020

Surrogate Modeling of Spent Fuel Degradation for Repository Performance Assessment

SIAM Conference on Computational Science and Engineering (CSE21)

Timothy M. Berg, Kyung Won Chang, Rosemary Claire Leone, Daniel Thomas Seidl, Paul Mariner, Bert Debusschere

Abstract – 2020 Abstract 2020

Quantum Annealing Approaches for Building Sparse Surrogate Models in Uncertainty Quantification

Bert Debusschere, Khachik Sargsyan, Timothy M. Berg, Ojas D. Parekh

SAND Report – 2019 SAND Report 2019
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