Apportion the uncertainty in the output to different sources of uncertainty in the input.
General idea of sensitivity analysis
State-of-the-art computational models often need to describe very complex systems, and hence also include a large number of parameters. One of the attractive features of physics-based models is that in those models (most) parameters have a clear physical meaning. Nevertheless, the determination of these parameters is often very elaborate and costly. Hence, it is essential to identify the most important parameters (worth the effort) for a particular problem at hand. In order to distinguish parameters which have a significant influence on a specific model output from non-influential parameters, sensitivity analysis is the method of choice.
Goal
The goal of sensitivity analysis is threefold:
- Identify the most influential parameters on which experimental studies should focus.
- Identify parameters with little to no effect, which can thus be set to fixed values within their range.
- Identify and quantify the interaction between parameters.
Meta-model based Sobol analysis
QUEENS allows to use the Sobol method as a global variance-based method combined with a Gaussian process used as a metamodel. The Sobol method requires a large number of model evaluations, which is prohibitive for computationally expensive models. Employing Gaussian processes as metamodels for the underlying full model allows to solve this problem. However, metamodelling ... Metamodelling introduces further uncertainty, which QUEENS also quantifies in this case.
Reprinted from (Wirthl et al., 2023), licensed under CC BY 4.0{:target="_blank"}.
Features in QUEENS
- Elementary effects (Morris method)
- Sobol method
- Sobol method based on a Gaussian process used as a metamodel