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Publication	Type	Year
PyApprox: Enabling efficient model analysis John Davis Jakeman https://www.osti.gov/search/identifier:1879614	SAND Report – 2022 SAND Report	2022
Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ Machine Learning and Deep Learning Conference Timothy Michael Wildey, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Owen Davis, Teresa Portone, Tian Yu NMN Yen, Bryan William Reuter, Alex Gorodetsky, Ahmad Rushdi, Daniele Schiavazzi, Lauren Partin	Conference Presentation – 2022 Conference Presentation	2022
Coupling optimal experimental design and optimal control World Congress on Computational Mechanics Rebekah Dale White, Bart G van Bloemen Waanders, John Davis Jakeman, Alen Alexanderian, Arvind Saibaba	Conference Presentation – 2022 Conference Presentation	2022
Machine learning in the context of inverse, control, and experimental design problems Machine learning and deep learning -- internal Sandia conference Rebekah Dale White, Bart G van Bloemen Waanders, John Davis Jakeman	Conference Presentation – 2022 Conference Presentation	2022
Overview and perspectives on multifidelity UQ Inria Platon projet-team seminar Gianluca Geraci, Michael S. Eldred, Alex Gorodetsky, John Davis Jakeman, Teresa Portone, Bryan William Reuter	Abstract – 2022 Abstract	2022
Bayesian risk-averse optimal experimental design with feedback UT Austin working group Rebekah Dale White, Bart G van Bloemen Waanders, Drew Philip Kouri, John Davis Jakeman, Alen Alexanderian	Presentation (non-conference) – 2022 Presentation (non-conference)	2022
Model Tuning for Multifidelity Sampling in Dakota 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) Michael S. Eldred, Gianluca Geraci, Bryan William Reuter, Teresa Portone, John Davis Jakeman, Alex A. Gorodetsky	Conference Presentation – 2022 Conference Presentation	2022
Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning Journal of Machine Learning Research Cosmin Safta, John Davis Jakeman, Alex Gorodetsky https://www.osti.gov/search/identifier:1872019	Journal Article – 2022 Journal Article	2022
PyApprox: Approximation and Probabilistic Analysis of Data SIAM Conference on Uncertainty Quantification John Davis Jakeman	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
Barlow twins reduced order modeling with uncertainty quantification for contact problems USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling Teeratorn Kadeethum, John Davis Jakeman, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon	Abstract – 2022 Abstract	2022
All-at-Once (and Bi-Level) Model Tuning for Multifidelity Sampling SIAM Conference on Uncertainty Quantification (UQ22) Michael S. Eldred, Gianluca Geraci, Teresa Portone, Alex A. Gorodetsky, John Davis Jakeman	Conference Presentation – 2022 Conference Presentation	2022
Quantifying Uncertainty in E3SM via Functional Tensor Network Approximations Siam Uq22 Cosmin Safta, John Davis Jakeman, Khachik Sargsyan	Conference Presentation – 2022 Conference Presentation	2022
Exploring risk-averse design criteria for sequential optimal experimental design in a Bayesian setting Siam Uq Rebekah Dale White, Bart G van Bloemen Waanders, John Davis Jakeman, Alexanderian Alen, Drew Philip Kouri	Conference Presentation – 2022 Conference Presentation	2022
Machine Learning And Uncertainty Quantification For Coupled Multi-physics, Multi-scale And Multi-fidelity Modeling X International Conference on Computational Methods for Coupled Problems Lorenzo Tamellini, John Davis Jakeman, Irina Kalashnikova Tezaur	Abstract – 2022 Abstract	2022
Automating Model Selection and Tuning for Multifidelity UQ (MFUQ) Siam Uq 22 James Warner, Geoffrey Bomarito, Gianluca Geraci, Michael S. Eldred, Marten Thompson, John Davis Jakeman, Patrick Leser, Paul Leser, Alex Gorodetsky	Conference Presentation – 2022 Conference Presentation	2022
Global Sensitivity Analysis Using the Ultra-Low Resolution Energy Exascale Earth System Model (E3SM) Siam Uq 2022 Irina Kalashnikova Tezaur, Kara J. Peterson, Amy Jo Powell, John Davis Jakeman, Erika Louise Roesler	Conference Presentation – 2022 Conference Presentation	2022
Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems International Journal For Numerical Methods In Engineering John Davis Jakeman, Sam Friedman, Michael S. Eldred, Lorenzo Tamellini, Alex Gorodestky, Doug Allaire https://www.osti.gov/search/identifier:1855808	Journal Article – 2022 Journal Article	2022
Identifying metrics in adaptive evaluation of impacts of factor fixing on objective functions under model structure uncertainty 2022 International Environmental Modelling and Software Society Biennial Meeting Qian Wang, Joseph Guillaume, John Davis Jakeman, Barry Croke, Tao Yang, Anthony Jakeman	Abstract – 2022 Abstract	2022
Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk Reliability Engineering & System Safety John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta https://www.osti.gov/search/identifier:1845389	Journal Article – 2022 Journal Article	2022
Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach Environmental Modelling & Software Qian (Australian National University) Wang, Joseph (Australian National University) Guillaume, John Davis Jakeman, Tao(Hohai University) Yang, Takuya (Australian National University) Iwanaga, Barry (Australian National University) Croke, Tony (Australian National University) Jakeman https://www.osti.gov/search/identifier:1845388	Journal Article – 2022 Journal Article	2022
Multi-fidelity machine learning and uncertainty quantification with PyApprox The 8th European Congress on Computational Methods in Applied Sciences and Engineering John Davis Jakeman	Abstract – 2021 Abstract	2021
Functional Tensor Network Approximations for E3SM Land Model AGU Fall Meeting 2021 Cosmin Safta, Khachik Sargsyan, John Davis Jakeman	Conference Presentation – 2021 Conference Presentation	2021
Model Tuning For Multifidelity Sampling In Dakota 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2022) Michael S. Eldred, Gianluca Geraci, Bryan William Reuter, Teresa Portone, John Davis Jakeman, Alex A. Gorodetsky	Abstract – 2021 Abstract	2021
Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning AIAA SciTech 2021 Geoffrey F. Bomarito, Gianluca Geraci, James E. Warner, Patrick E. Leser, William P. Leser, Michael S. Eldred, John Davis Jakeman, Alex A. Gorodetsky	Conference Proceeding – 2021 Conference Proceeding	2021
Document Title	Type	Year

Results 1–25 of 223

Publications

PyApprox: Enabling efficient model analysis

Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ

Coupling optimal experimental design and optimal control

Machine learning in the context of inverse, control, and experimental design problems

Overview and perspectives on multifidelity UQ

Bayesian risk-averse optimal experimental design with feedback

Model Tuning for Multifidelity Sampling in Dakota

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning

PyApprox: Approximation and Probabilistic Analysis of Data

Improving Digital Twins by Learning from a Fleet of Assets

Barlow twins reduced order modeling with uncertainty quantification for contact problems

All-at-Once (and Bi-Level) Model Tuning for Multifidelity Sampling

Quantifying Uncertainty in E3SM via Functional Tensor Network Approximations

Exploring risk-averse design criteria for sequential optimal experimental design in a Bayesian setting

Machine Learning And Uncertainty Quantification For Coupled Multi-physics, Multi-scale And Multi-fidelity Modeling

Automating Model Selection and Tuning for Multifidelity UQ (MFUQ)

Global Sensitivity Analysis Using the Ultra-Low Resolution Energy Exascale Earth System Model (E3SM)

Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems

Identifying metrics in adaptive evaluation of impacts of factor fixing on objective functions under model structure uncertainty

Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach

Multi-fidelity machine learning and uncertainty quantification with PyApprox

Functional Tensor Network Approximations for E3SM Land Model

Model Tuning For Multifidelity Sampling In Dakota

Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning