A Robust and Efficient Probabilistic Approach for Challenging Industrial Applications with High-Dimensional and Non-Monotonic Design Spaces

Abstract

The objective of this paper is to apply state -of -the -art meta -modeling techniques to achieve more efficient and robust probabilistic analy sis for challenging industrial applications with high dimensional and non -monotonic design spaces. The proposed approach enables Cumulative Distribution Function (CDF) and Probability Density Function (PDF) calculations in design spaces that are monotonic or non -monotonic and have a large number of variables (100+). The proposed method includes 1) constructing an accurate and fast running meta -model from a small number of training points; 2) applying a large number of Monte Carlo runs to the meta -model; 3) post -processing the Monte Carlo output in a special way so that accurate CDF and PDF curves and other probabilistic information are obtained. Since accurate meta -models can be constructed for design spaces that are non -monotonic or have a very large numbe r of variables (100+), this approach provides a practical general -purpose solution process that is applicable to most probabilistic design problems encountered in industry.