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Uncertainty quantification (UQ) is the science of 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.
Conformal prediction (CP) is a machine learning framework for uncertainty quantification that produces statistically valid prediction regions (prediction intervals) for any underlying point predictor (whether statistical, machine, or deep learning) only assuming exchangeability of the data. CP works by computing nonconformity scores on ...
Identify the model output to be analysed (the target of interest should ideally have a direct relation to the problem tackled by the model). Run the model a number of times using some design of experiments, [15] dictated by the method of choice and the input uncertainty. Using the resulting model outputs, calculate the sensitivity measures of ...
Quantification of Margins and Uncertainty (QMU) is a decision support methodology for complex technical decisions. QMU focuses on the identification, characterization, and analysis of performance thresholds and their associated margins for engineering systems that are evaluated under conditions of uncertainty, particularly when portions of those results are generated using computational ...
A machine learning model is a type of ... Bayesian approaches in order to incorporate ignorance and uncertainty quantification. ... architecture search, and ...
Data Mining and Uncertainty Quantification (DMQ) The Data Mining and Uncertainty Quantification group makes use of technology from the fields of High Performance Computing and Uncertainty Quantification in order to quantify uncertainties in large data sets towards reliable insights in Data Mining.
Empirical gramians can be computed for linear and nonlinear control systems for purposes of model order reduction, uncertainty quantification or system identification. The emgr framework is a compact open source toolbox for gramian-based model reduction and compatible with OCTAVE and MATLAB.
In physical experiments uncertainty analysis, or experimental uncertainty assessment, deals with assessing the uncertainty in a measurement.An experiment designed to determine an effect, demonstrate a law, or estimate the numerical value of a physical variable will be affected by errors due to instrumentation, methodology, presence of confounding effects and so on.