Uncertainty evaluation for computationally expensive models
Description
Finite element methods for the analysis of flow measurements
is an example for computationally expensive models
In the analysis of computationally expensive problems the model function $$\mathbf{Y}=F(\mathbf{X})$$ is typically considered as a 'black box', so that evaluation of $F$ is possible, but evaluation of its derivatives with respect to the inputs is not. In computationally expensive models the evaluation of $F$ requires a significant amount of computer calculation time. This makes a straightforward application of Monte Carlo sampling for uncertainty evaluation impractical.
To this end, many approaches to uncertainty evaluation for expensive models consider alternative sampling methods. Examples are stratified sampling, importance sampling or Latin hypercube sampling, where the selection of realisations drawn from the input quantities follows a certain rule. The goal is to reduce the number of samples that have to be propagated by selecting representative samples.
A different approach is that of so called 'polynomial chaos', which was first used by Wiener in 1938 in the context of statistical mechanics. The main idea is that every stochastic quantity with finite second moment can be expanded as a sum of a set of orthogonal polynomials in an appropriate set of random variables. This set of of orthogonal polynomials of a random variable is called polynomial chaos. Its main advantage is that one can obtain a good representation of the complete stochastic behaviour of the outputs for fewer samples than are required by other methods. More information on polynomial chaos can be found here.
Instead of using smarter sampling methods one may replace the complex model function by a so called surrogate model. Examples are nearest neighbour interpolation and Gaussian process emulation and polynomial chaos.
To this end, many approaches to uncertainty evaluation for expensive models consider alternative sampling methods. Examples are stratified sampling, importance sampling or Latin hypercube sampling, where the selection of realisations drawn from the input quantities follows a certain rule. The goal is to reduce the number of samples that have to be propagated by selecting representative samples.
A different approach is that of so called 'polynomial chaos', which was first used by Wiener in 1938 in the context of statistical mechanics. The main idea is that every stochastic quantity with finite second moment can be expanded as a sum of a set of orthogonal polynomials in an appropriate set of random variables. This set of of orthogonal polynomials of a random variable is called polynomial chaos. Its main advantage is that one can obtain a good representation of the complete stochastic behaviour of the outputs for fewer samples than are required by other methods. More information on polynomial chaos can be found here.
Instead of using smarter sampling methods one may replace the complex model function by a so called surrogate model. Examples are nearest neighbour interpolation and Gaussian process emulation and polynomial chaos.
Research
Smart sampling
Smart sampling methods (such as polynomial chaos and Latin hypercube sampling) can drastically reduce the number of function evaluations and the resulting computational cost required for uncertainty evaluation compared to random sampling. Many sampling methods have specific assumptions (such as the inputs must be independent, the output quantity can be locally approximated as a polynomial function of the input quantities), which can limit their applicability. Thus, smart sampling methods have to be adapted and tested for their suitability for application in metrology.
Surrogate models
While the smart sampling methods try to minimise the computational expense by making the number of function evaluations needed for determination of uncertainties as small as possible, surrogate models replace the computationally expensive model by an approximate computationally cheap model such as polynomial or spline models. This involves a computationally expensive preprocessing step that samples the parameter space of the original physical model in order to derive the surrogate model.
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