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Publication	Type	Year
Multi-task Machine Learning for Fusion Simulations USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling William Lewis, Kathryn Anne Maupin, Anh Tran, Patrick Knapp	Conference Poster – 2022 Conference Poster	2022
Supporting Large-Scale Experimental Design Decisions in the Presence of Uncertainty CIS LDRD Proposal Kathryn Anne Maupin	Presentation (non-conference) – 2022 Presentation (non-conference)	2022
Multi-Output Surrogate Construction for Fusion Simulations CRLC Seminar Series Kathryn Anne Maupin	Abstract – 2022 Abstract	2022
Multi-Output Surrogate Construction for Fusion Simulations SIAM Conference on Uncertainty Quantification Kathryn Anne Maupin, Anh Tran, Michael Edwin Glinsky, William Lewis	Conference Presentation – 2022 Conference Presentation	2022
An Adaptive Selection of Predictive Computational Models USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (MLIP) Danial Faghihi, Jingye Tan, Kathryn Anne Maupin, Baoshan Liang	Abstract – 2022 Abstract	2022
Integrated Computational Materials Engineering with Monotonic Gaussian Processes ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Anh Tran, Kathryn Anne Maupin, Theron Rodgers	Conference Paper – 2022 Conference Paper	2022
Physics-Constrained Multi-Task Machine Learning for Fusion Simulations ECCOMAS Congress 2022 Anh Tran, Kathryn Anne Maupin, William Lewis, Michael Edwin Glinsky, Patrick Knapp	Abstract – 2021 Abstract	2021
Informing Missing Physics with Model Form Error and Model Selection 8th European Congress on Computational Methods in Applied Sciences and Engineering Kathryn Anne Maupin, Jaideep Ray, Teresa Portone	Abstract – 2021 Abstract	2021
Machine Learning for Single Particle Motion in Plasmas SIAM Conference on Uncertainty Quantification Sonata Valaitis, Davide Curreli, Kathryn Anne Maupin	Abstract – 2021 Abstract	2021
Multi-Output Surrogate Construction for Fusion Simulations SIAM Conference on Uncertainty Quantification Kathryn Anne Maupin, Anh Tran	Abstract – 2021 Abstract	2021
Physics-Informed and Data-Driven Predictive Models with Quantified Uncertainty SIAM Conference on Uncertainty Quantification in Atlanta Anh Tran, Kathryn Anne Maupin	Abstract – 2021 Abstract	2021
Quantifying Predictive Uncertainty with Physics-Informed Machine Learning SIAM Conference on Uncertainty Quantification in Atlanta Anh Tran, Kathryn Anne Maupin	Abstract – 2021 Abstract	2021
An Adaptive Framework for Determining Optimal Valid Model: Application to Size Dependent Plasticity 19th U.S. National Congress on Theoretical and Applied Mechanics Danial Faghihi, Jingye Tan, Kathryn Anne Maupin, Baoshan Liang	Abstract – 2021 Abstract	2021
A Bayesian Machine Learning Framework for Selection of the Strain Gradient Plasticity Multiscale Model International Mechanical Engineering Congress & Exposition Jingye Tan, Kathryn Anne Maupin, Danial Faghihi	Conference Presentation – 2021 Conference Presentation	2021
Probabilistic methods for model inadequacy Eccomas 2022 Teresa Portone, Kathryn Anne Maupin, Rebecca Morrison	Abstract – 2021 Abstract	2021
Multi-Output Surrogate Construction for Fusion Simulations 63rd Annual Meeting of the APS Division of Plasma Physics Kathryn Anne Maupin, Anh Tran, Michael Edwin Glinsky, Patrick Knapp, William Lewis	Abstract – 2021 Abstract	2021
Integration of first-principles models and machine-learning algorithms for computational efficiency in tokamak physics simulations 63rd Annual Meeting of the APS Division of Plasma Physics Sonata Valaitis, Kathryn Anne Maupin, Davide Curreli	Abstract – 2021 Abstract	2021
Multi-Output Surrogate Construction for Fusion Simulations US National Congress on Computational Mechanics Kathryn Anne Maupin, Anh Tran, Michael Edwin Glinsky	Conference Presentation – 2021 Conference Presentation	2021
A Bayesian Framework for Validation and Selection of Multiscale Plasticity Models with Quantified Uncertainty US National Congress on Computational Mechanics Jingye Tan, Kathryn Anne Maupin, Danial Faghihi https://www.osti.gov/search/identifier:1879289	Conference Presentation – 2021 Conference Presentation	2021
Calibration and Propagation of Model Form Error Across Experimental Settings Computing Research Leadership Council Seminar, UC Merced Kathryn Anne Maupin https://www.osti.gov/search/identifier:1866901	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
A Bayesian Machine Learning Framework For Selection Of The Strain Gradient Plasticity Multiscale Model ASME 2021 International Mechanical Engineering Congress & Exposition Kathryn Anne Maupin, Jingye Tan, Shuai Shao, Danial Faghihi https://www.osti.gov/search/identifier:1865574	Conference Paper – 2021 Conference Paper	2021
A Physics-Based Machine Learning Framework for Validation and Selection of Multiscale Models of Microscale Plasticity International Mechanical Engineering Congress & Exposition Kathryn Anne Maupin, Danial Faghihi, Jingye Tan, Baoshan Liang	Abstract – 2021 Abstract	2021
Use of Model Discrepancy and Model Selection as a Means of Informing Missing Physics SIAM Conference on Computational Science and Engineering Kathryn Anne Maupin, Teresa Portone https://www.osti.gov/search/identifier:1848042	Conference Presentation – 2021 Conference Presentation	2021
Multi-Output Surrogate Construction for Fusion Simulations 16th U.S. National Congress on Computational Mechanics Kathryn Anne Maupin, Anh Tran	Abstract – 2021 Abstract	2021
A Bayesian Framework for Validation and Selection of Multiscale Plasticity Models with Quantified Uncertainty 16th U.S. National Congress on Computational Mechanics Jingyye Tan, Titiksha Singh, Danial Faghihi, Kathryn Anne Maupin	Abstract – 2021 Abstract	2021
Document Title	Type	Year

Results 1–25 of 62

Publications

Multi-task Machine Learning for Fusion Simulations

Supporting Large-Scale Experimental Design Decisions in the Presence of Uncertainty

Multi-Output Surrogate Construction for Fusion Simulations

Multi-Output Surrogate Construction for Fusion Simulations

An Adaptive Selection of Predictive Computational Models

Integrated Computational Materials Engineering with Monotonic Gaussian Processes

Physics-Constrained Multi-Task Machine Learning for Fusion Simulations

Informing Missing Physics with Model Form Error and Model Selection

Machine Learning for Single Particle Motion in Plasmas

Multi-Output Surrogate Construction for Fusion Simulations

Physics-Informed and Data-Driven Predictive Models with Quantified Uncertainty

Quantifying Predictive Uncertainty with Physics-Informed Machine Learning

An Adaptive Framework for Determining Optimal Valid Model: Application to Size Dependent Plasticity

A Bayesian Machine Learning Framework for Selection of the Strain Gradient Plasticity Multiscale Model

Probabilistic methods for model inadequacy

Multi-Output Surrogate Construction for Fusion Simulations

Integration of first-principles models and machine-learning algorithms for computational efficiency in tokamak physics simulations

Multi-Output Surrogate Construction for Fusion Simulations

A Bayesian Framework for Validation and Selection of Multiscale Plasticity Models with Quantified Uncertainty

Calibration and Propagation of Model Form Error Across Experimental Settings

A Bayesian Machine Learning Framework For Selection Of The Strain Gradient Plasticity Multiscale Model

A Physics-Based Machine Learning Framework for Validation and Selection of Multiscale Models of Microscale Plasticity

Use of Model Discrepancy and Model Selection as a Means of Informing Missing Physics

Multi-Output Surrogate Construction for Fusion Simulations

A Bayesian Framework for Validation and Selection of Multiscale Plasticity Models with Quantified Uncertainty