Live briefing
Everything about Uncertainty quantification
Science of characterizing uncertainties
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. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc., will lead to different results that can only be predicted in a statistical sense. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Uncertainty quantificationA 5-minute orientation
- Skim a recent paper: Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisonsInfluential work since 2022
- Pick up a classic book: Model Validation and Uncertainty Quantification, Volume 3The most-reprinted book on the subject
- Read the landmark paper: A review of uncertainty quantification in deep learning: Techniques, applications and challengesCited 2,762 times
- Go sideways: Hyperbolastic functionsA closely related topic
- Go sideways: Point estimationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification in chemical systems
Optimal Uncertainty Quantification
Uncertainty quantification in drug design
Uncertainty Quantification in Aeroelasticity
Uncertainty quantification and film cooling
Uncertainty Quantification for Multiscale Simulations1
Uncertainty quantification in simulation science
🆕 Influential since 2022
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification
Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis
Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
📚 Books worth reading
Model Validation and Uncertainty Quantification, Volume 3H. Sezer Atamturktur, Babak Moaveni · 2014
Model Validation and Uncertainty Quantification, Volume 3Robert Barthorpe · 2017- 📕Uncertainty Quantification in Variational InequalitiesBaasansuren Jadamba, Akhtar Khan · 2019
Uncertainty Quantification in Laminated CompositesSudip Dey, Tanmoy Mukhopadhyay · 2018
Model Validation and Uncertainty Quantification, Volume 3Zhu Mao · 2020- 📕Uncertainty Quantification of Stochastic Defects in MaterialsLiu Chu · 2021
- 📕Advanced Computational VibroacousticsRoger Ohayon, Christian Soize · 2014
Topics in Model Validation and Uncertainty Quantification, Volume 5Todd Simmermacher · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Hyperbolastic functions The hyperbolastic functions, also known as hyperbolastic growth models, are mathematical functions that are used in medical statistical modeling. These…
- Point estimation In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate, since it identifies a point rather…
- Loss function In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an…
- Bayesian history matching Bayesian history matching is a statistical method for calibrating complex computer models. The equations inside many scientific computer models contain…
- Laplace's approximation Laplace's approximation or the quadratic approximation (QUAP) provides an analytical expression for a posterior probability distribution by fitting a Gaussian…
- Generalized linear array model In statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. It based on the generalized linear model…
- Bayesian linear regression Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables…
- MINQUE In statistics, the theory of minimum norm quadratic unbiased estimation (MINQUE) was developed by C. R. Rao. MINQUE is a theory alongside other estimation…