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Publication	Type	Year
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 Presentation – 2021 Conference Presentation	2021
Leveraging Multiple Information Sources to Enable High-Fidelity Uncertainty Quantification AE585 Chair's Distinguished Lecture Series Michael S. Eldred, Gianluca Geraci, Gorodetsky, John Davis Jakeman	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Improving Digital Twins by Learning from a Fleet of Assets SIAM Conference on Uncertainty Quantification (UQ22) Daniel Thomas Seidl, John Davis Jakeman	Abstract – 2021 Abstract	2021
PyApprox: Approximation and probabilistic analysis of data Siam Conference on Uncertainty Quantification John Davis Jakeman	Abstract – 2021 Abstract	2021
Automating Model Selection and Tuning for Multifidelity UQ SIAM Conference on Uncertainty Quantification (UQ22) James Warner, Geoffrey Bomarito, Patrick Leser, William Leser, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman	Abstract – 2021 Abstract	2021
The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact NASA Langley HPC Seminar Series Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Teresa Portone, Timothy Michael Wildey, Ahmad Rushdi, Daniel Thomas Seidl	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta	Conference Presentation – 2021 Conference Presentation	2021
Improving Digital Twins by Learning from a Fleet of Assets Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Daniel Thomas Seidl, John Davis Jakeman	Conference Presentation – 2021 Conference Presentation	2021
Characterizing Approximation Methods for Digital Twins in Scientific Computing Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Zachary Benjamin Morrow, Bart G van Bloemen Waanders, John Davis Jakeman	Conference Presentation – 2021 Conference Presentation	2021
Exploring risk-averse design criteria for sequential optimal experimental design in a Bayesian setting Mechanistic Machine Learning and Digital Twins for Compuational Science, Engineering & Technology Rebekah Dale White, John Davis Jakeman, Bart G van Bloemen Waanders, Drew Philip Kouri, Alex Alexanderian	Conference Presentation – 2021 Conference Presentation	2021
Data-driven learning of non-autonomous systems SIAM Journal on Scientific Computing Tong Qin, Zhen Chen, John Davis Jakeman, Dongbin Xiu	Journal Article – 2021 Journal Article	2021
Advanced multilevel and multifidelity UQ strategies: applications, generalized model hierarchies, and data-driven approaches SIAM Conference of Uncertainty Quantification (UQ22) Teresa Portone, John Davis Jakeman, Alex Arkady Gorodetsky	Abstract – 2021 Abstract	2021
Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report Drew Philip Kouri, John Davis Jakeman, Jose Gabriel Huerta, Timothy Walsh, Chandler Baldwin Smith, Stan Uryasev https://www.osti.gov/search/identifier:1820307	SAND Report – 2021 SAND Report	2021
MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources Computational Mechanics Alex Arkady Gorodetsky, John Davis Jakeman, Gianluca Geraci https://www.osti.gov/search/identifier:1820427	Journal Article – 2021 Journal Article	2021
Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science US National Congress on Computational Mechanics Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Anh Tran	Conference Presentation – 2021 Conference Presentation	2021
Efficient Deployment of Multifidelity Sampling Methods in Production Settings Usnccm16 Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Teresa Portone	Conference Presentation – 2021 Conference Presentation	2021
Adaptive Basis for Multifidelity Uncertainty Quantification 16th US National Congress on Computational Mechanics Xiaoshu Zeng, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Alex Gorodetsky, Roger Ghanem	Conference Presentation – 2021 Conference Presentation	2021
Multi-fidelity Machine Learning Machine Learning and Deep Learning Conference John Davis Jakeman, Michael S. Eldred, Gianluca Geraci, Teresa Portone, Ahmad Rushdi, Daniel Thomas Seidl, Thomas M. Smith https://www.osti.gov/search/identifier:1876608	Conference Presentation – 2021 Conference Presentation	2021
Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity IX International Conference on Coupled Problems in Science and Engineering John Davis Jakeman, Samuel Friedman, Michael S. Eldred, Lorenzo Tamellini, Alex Gorodetsky, Doug Allaire https://www.osti.gov/search/identifier:1872879	Conference Presentation – 2021 Conference Presentation	2021
Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta https://www.osti.gov/search/identifier:1807455	Report – 2021 Report	2021
Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity Sam Friedman, John Davis Jakeman, Michael S. Eldred, Lorenzo Tamellini, Alex Gorodestky, Doug Allaire https://www.osti.gov/search/identifier:1807453	Report – 2021 Report	2021
Bedrock Inversion and Hyper Differential Sensitivity Analysis for the Shallow Ice Model Numerical Analysis in Data Science Transition Workshop William Mcneil Reese, Joseph Lee Hart, Bart G van Bloemen Waanders, Mauro Perergo, John Davis Jakeman, Arvind Saibaba	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem Emi/pmc 2021 Timothy Michael Wildey, Troy Butler, John Davis Jakeman https://www.osti.gov/search/identifier:1877851	Conference Presentation – 2021 Conference Presentation	2021
Digital Twin Modeling with Gaussian Process Networks Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Daniel Thomas Seidl, John Davis Jakeman	Abstract – 2021 Abstract	2021
Risk-Adapted Surrogate Modeling Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology John Davis Jakeman, Drew Philip Kouri, Jose Gabriel Huerta	Abstract – 2021 Abstract	2021
Document Title	Type	Year

Results 26–50 of 223

Publications

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

Leveraging Multiple Information Sources to Enable High-Fidelity Uncertainty Quantification

Improving Digital Twins by Learning from a Fleet of Assets

PyApprox: Approximation and probabilistic analysis of data

Automating Model Selection and Tuning for Multifidelity UQ

The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact

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

Improving Digital Twins by Learning from a Fleet of Assets

Characterizing Approximation Methods for Digital Twins in Scientific Computing

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

Data-driven learning of non-autonomous systems

Advanced multilevel and multifidelity UQ strategies: applications, generalized model hierarchies, and data-driven approaches

Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report

MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources

Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science

Efficient Deployment of Multifidelity Sampling Methods in Production Settings

Adaptive Basis for Multifidelity Uncertainty Quantification

Multi-fidelity Machine Learning

Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity

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

Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity

Bedrock Inversion and Hyper Differential Sensitivity Analysis for the Shallow Ice Model

Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem

Digital Twin Modeling with Gaussian Process Networks

Risk-Adapted Surrogate Modeling