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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25

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DOE OSTI2025

Uncertainty Quantification in Computational Science: from Physical Models to Neural Networks

Tutorial at the upcoming BIRS workshop https://www.birs.ca/events/2025/5-day-workshops/25w5381/schedule

Sargsyan, Khachik [Sandia National Laboratories (S↗

DOE OSTI2025

Panel: Shared Opportunities in Uncertainty Quantification in Support of CFD Validation

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Lance, Blake [Sandia National Laboratories (SNL-NM↗

DOE OSTI2025

Scientific Machine Learning in Enhancing Model Predictability Through Model Form Error Quantification.

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Ohene-Obeng, Kwesi Appau [Sandia National Laborato↗

DOE OSTI2025

Reduced-Basis Methods for Uncertainty Quantification with application to High-Speed Flows

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Blonigan, Patrick Joseph [Sandia National Laborato↗

DOE OSTI2026

Final Technical Report: Probabilistic Learning, Dimension Reduction, and Spectral Representations for High-Dimensional Uncertainty Quantification

Final Technical Report

Ghanem, Roger [University of Southern California] ↗

DOE OSTI2025

Model credibility and risk quantification

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Karve, Pranav M. [Vanderbilt University]↗

DOE OSTI2025

Uncertainty Quantification of Transport coefficient datasets in simulations of warm dense matter

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Stanek, Lucas James [Sandia National Laboratories ↗

DOE OSTI2025

Monte Carlo uncertainty quantification and sensitivity analysis for the C5G7 benchmark

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Clements, Kayla [Oregon State University ]↗

DOE OSTI2025

NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds

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Schiavazzi, Daniele [University of Notre Dame]↗

DOE OSTI2025

Stein variational inference and sparsification for uncertainty quantification of physical neural networks

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Jones, Reese Edward [Sandia National Laboratories ↗

DOE OSTI2026

Uncertainty Quantification (UQ) for AI fact Sheet

UQ is traditionally defined as quantitative characterization and estimation of uncertainties in both computational and real-world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026