DeepAI AI Chat
Log In Sign Up

Uncertainty Quantification in Case of Imperfect Models: A Review

by   Sebastian Kersting, et al.

Uncertainty quantification of complex technical systems is often based on a computer model of the system. As all models such a computer model is always wrong in the sense that it does not describe the reality perfectly. The purpose of this article is to give a review of techniques which use observed values of the technical systems in order to take into account the inadequacy of a computer model in uncertainty quantification. The techniques reviewed in this article are illustrated and compared by applying them to applications in mechanical engineering.


Minimum Discrepancy Methods in Uncertainty Quantification

The lectures were prepared for the École Thématique sur les Incertitudes...

Lowering the Entry Bar to HPC-Scale Uncertainty Quantification

Treating uncertainties in models is essential in many fields of science ...

Spatial Statistics

Spatial statistics is an area of study devoted to the statistical analys...

Adaptive reconstruction of imperfectly-observed monotone functions, with applications to uncertainty quantification

Motivated by the desire to numerically calculate rigorous upper and lowe...

I Find Your Lack of Uncertainty in Computer Vision Disturbing

Neural networks are used for many real world applications, but often the...

Bayesian Emulation for Computer Models with Multiple Partial Discontinuities

Computer models are widely used across a range of scientific disciplines...

Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modeling

Disorders of coronary arteries lead to severe health problems such as at...