SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Use of Uncertainty Quantification Methods to Calibrate and Validate a Mesoscale Selective Laser Melting Model.
Abstract not provided.
Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids
We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.
UQTk A Flexible Python/C++ Toolkit for Uncertainty Quantification.
Abstract not provided.
Uncertainty Quantification of Velocimetry-Based Load Current Inferences for 100 ns Multi-Mega-Amp Pulsed Power Experiments.
Abstract not provided.
Uncertainty Quantification and Bayesian Calibration of a Powder-Scale Model for Selective Laser Melting.
Abstract not provided.
GEOMETRIC UNCERTAINTY QUANTIFICATION AND ROBUST DESIGN FOR 2D SATELLITE SHIELDING.
Abstract not provided.
Verification Validation and Uncertainty Quantification Hierarchy.
Abstract not provided.
Surrogate modeling and uncertainty quantification in models of physical systems.
Abstract not provided.
Uncertainty Quantification in Large Scale Computational Models.
Abstract not provided.
Continued Multilevel-Multifidelity Uncertainty Quantification of the SWiFT Wind Turbines.
Abstract not provided.
Verification of Uncertainty Quantification\\for Equation of State Table.
Abstract not provided.
Uncertainty Quantification in Large Scale Computational Models.
Abstract not provided.
Volumetric Anomaly Detection in X-ray CT Scans through Uncertainty Quantification.
Abstract not provided.
Predictive Uncertainty Quantification: Reinterpreting Machine Learning as Machine Prediction from the Bayesian Perspective.
Abstract not provided.
Deep Learning with Per-Voxel Uncertainty Quantification for Volumetric Segmentation of Battery Electrode Images.
Abstract not provided.