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Uncertainty quantification (UQ) is essential for producing informative computational predictions in a wide range of sciences and engineering. The field relies on a broad range of mathematical and stat...
Consider approximating a function $f$ by an emulator $\hat{f}$ based on $n$ observations of $f$. This problem is a common when observing $f$ requires a computationally demanding simulation or an actua...
Uncertainty quantification and weak approximation of an elliptic inverse problem
Uncertainty quantification weak approximation elliptic inverse problem
2011/3/18
We consider the inverse problem of determining the permeability from the pressure in a Darcy model of flow in a porous medium. Mathematically the problem is to find the diffusion coefficient for a lin...
We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront.
Uncertainty quantification in complex systems using approximate solvers
uncertainty quantification Monte Carlo Bayesian nonparametric regression
2010/4/30
This paper proposes a novel uncertainty quantification framework for computationally
demanding systems characterized by a large vector of non-Gaussian uncertainties. It combines
state-of-the-art tec...