Machine Learning for Uncertainty Quantification (UQ): Trusting the Black Box
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James Warner↗
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Results for “Uncertainty quantification”
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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Uncertainty Quantification in the Architecture Assessment process for the Mass Change Designated Observable Study
No abstract provided
Wiese, David↗
The relationship between Uncertainty Quantification and Observations in Ice Sheet Modeling
No abstract provided
Morlighem, Mathieu↗
Post-hoc Uncertainty Quantification for Remote Sensing Observing Systems
No abstract provided
Gunson, Michael↗
Tectonic Displacement Mapping from SAR Offset Time Series: Noise Reduction and Uncertainty Quantification
No abstract provided
Simons, Mark↗
An Introduction to Uncertainty Quantification: Transitioning from Research to Practice
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Jim Warner↗
Uncertainty Quantification (UQ) for Modeling & Simulation at NASA
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James Warner↗
Post hoc Uncertainty Quantification for Remote Sensing Observing Systems
No abstract provided
Gunson, Michael↗
Spatio-temporal models for uncertainty quantification in ice-sheet models using Gaussian random fields
No abstract provided
Larour, Eric↗
Post-hoc Uncertainty Quantification for Remote Sensing Observing Systems
No abstract provided
Gunson, Michael↗
Introduction to Uncertainty Quantification for Modeling and Simulation
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Jim Warner↗
Uncertainty Quantification for Interpretable Constitutive Models using Genetic Programming based Symbolic Regression
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Donovan Birky↗
AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles
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Steven Snyder↗
Air Quality Forecasting With Uncertainty Quantification By Fusing Model, Satellite, and in-Situ Data
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Carl Malings↗
Air Quality Forecasting with Uncertainty Quantification by Fusing Model, Satellite, and in-Situ Data
The goal of our project is to integrate diverse global and local air quality data sources using the cloud computing platform of Google Earth Engine to provide synthesized estimates and forecasts of air quality at a local scale but with a global scope freely accessible by air quality managers worldwide, facilitating their decision-making processes.
Carl Malings↗
Uncertainty Quantification Methodology for Sonic Boom Loudness of a Low-Boom Supersonic Concept
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Laura White↗
Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification
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Richard E. L. Higgins↗