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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.
Dec 1, 2021 · Volume 76, December 2021, Pages 243-297. A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Author links open overlay panel. Moloud Abdar a. , Farhad Pourpanah b. , Sadiq Hussain c. , Dana Rezazadegan d. , Li Liu e, Mohammad Ghavamzadeh f. , Paul Fieguth g. , Xiaochun Cao h, Abbas Khosravi a. ,
- Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul ...
- 2021
A course on the theory and methods of quantifying uncertainty in engineering models and simulations. Learn about verification, validation, aleatory and epistemic uncertainty, probability, approximation theory, and more.
UQ is a tool that connects theory, experimentation, and computation to understand and manage uncertainties in scientific discovery and decision-making. Learn about UQ methods, applications, and research opportunities in the UQ Lab.
Uncertainty Quantification is the process of converting epistemic uncertainties to aleatory ones. From a systems perspective, we typically employ models of real world processes. These models may be empirical or be realizations from theory.
A comprehensive and rigorous introduction to the mathematics and statistics of uncertainty quantification, with examples and exercises. The book covers topics such as measure and probability theory, inverse problems, sensitivity analysis, spectral expansions, and distributional uncertainty.
Uncertainty quantification (UQ) is a process that aims at quantitatively describing the origin, characterization, and propagation of different sources of uncertainty in complex systems.
