Sensitivity analysis using the Metamodel of Optimal Prognosis

TL;DR

This paper introduces an automatic method for selecting optimal meta-models and reducing variable space to enable efficient sensitivity analysis in complex virtual prototyping simulations.

Contribution

It presents an automated approach combining variable reduction and meta-model selection for high-dimensional sensitivity analysis.

Findings

Effective variable space reduction improves sensitivity analysis accuracy.

Meta-model selection enhances computational efficiency.

The approach supports probabilistic analysis in complex models.

Abstract

In real case applications within the virtual prototyping process, it is not always possible to reduce the complexity of the physical models and to obtain numerical models which can be solved quickly. Usually, every single numerical simulation takes hours or even days. Although the progresses in numerical methods and high performance computing, in such cases, it is not possible to explore various model configurations, hence efficient surrogate models are required. Generally the available meta-model techniques show several advantages and disadvantages depending on the investigated problem. In this paper we present an automatic approach for the selection of the optimal suitable meta-model for the actual problem. Together with an automatic reduction of the variable space using advanced filter techniques an efficient approximation is enabled also for high dimensional problems. This filter techniques enable a reduction of the high dimensional variable space to a much smaller subspace where meta-model-based sensitivity analyses are carried out to assess the influence of important variables and to identify the optimal subspace with corresponding surrogate model which enables the most accurate probabilistic analysis. For this purpose we investigate variance-based and moment-free sensitivity measures in combination with advanced meta-models as moving least squares and kriging.