Scientific Machine Learning in Enhancing Model Predictability Through Model Form Error Quantification.
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Ohene-Obeng, Kwesi Appau [Sandia National Laborato↗
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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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Reduced-Basis Methods for Uncertainty Quantification with application to High-Speed Flows
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Blonigan, Patrick Joseph [Sandia National Laborato↗
Final Technical Report: Probabilistic Learning, Dimension Reduction, and Spectral Representations for High-Dimensional Uncertainty Quantification
Final Technical Report
Ghanem, Roger [University of Southern California] ↗
Model credibility and risk quantification
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Karve, Pranav M. [Vanderbilt University]↗
Uncertainty Quantification of Transport coefficient datasets in simulations of warm dense matter
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Stanek, Lucas James [Sandia National Laboratories ↗
Monte Carlo uncertainty quantification and sensitivity analysis for the C5G7 benchmark
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Clements, Kayla [Oregon State University ]↗
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds
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Schiavazzi, Daniele [University of Notre Dame]↗
Stein variational inference and sparsification for uncertainty quantification of physical neural networks
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Jones, Reese Edward [Sandia National Laboratories ↗
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↗
Creation and Quantification of 3D Performance Surface FRFs
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Roettgen, Daniel Robert↗
Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model
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Narvaez, Karla Tame [Fermilab] (ORCID:000000022249↗
Quantification of void like defects using X-ray tomography in elastomers exposed to different hydrogen environments
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Mills, Bernice E. [Sandia National Laboratories (S↗
Evaluating Fastener Joint Performance: Advances in Modeling and Uncertainty Quantification From the Sandia Mechanics Challenge
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Mersch, John Philip [Sandia National Laboratories ↗
Neural operators for accelerated uncertainty quantification in ice-sheet modeling
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Perego, Mauro [Sandia National Laboratories (SNL-N↗
Advancing Nuclear Data Uncertainty Quantification: EMU and Machine Learning
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Astronomy and AstroPhysics↗
Latent trajectory analysis for uncertainty quantification in generative AI
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Krofcheck, Daniel Joseph [Sandia National Laborato↗
Separable Uncertainty Quantification in Materials Experiments with Variational Deep Material Networks
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Robertson, Andreas Euan [Sandia National Laborator↗
Pairing Simulation with Uncertainty Quantification for Exploding Wire Tests
In a 2025 SAND report by Carnes et al., it was observed that experimental and model results were not in agreement, especially peak voltage, and time of peak, for gold, with observed disagreements of up to 40%. In this work, we investigate whether other sources of uncertainty in the model can explain this disagreement between experiment and model.
Mendoza, Hector Javier [Sandia National Laboratori↗