Surrogate modeling for probability distribution estimation:uniform or adaptive design?

TL;DR

This study evaluates active learning-based surrogate models for estimating full probability distributions, finding that uniform designs often outperform AL, and the best methods depend on problem features and accuracy requirements.

Contribution

It provides a comprehensive comparison of AL and uniform designs for distribution estimation, highlighting their relative strengths and optimal configurations.

Findings

AL techniques do not systematically outperform uniform designs.

The choice of surrogate modeling methods depends on problem features and accuracy needs.

Performance varies based on local nonlinearity and computational budget.

Abstract

The active learning (AL) technique, one of the state-of-the-art methods for constructing surrogate models, has shown high accuracy and efficiency in forward uncertainty quantification (UQ) analysis. This paper provides a comprehensive study on AL-based global surrogates for computing the full distribution function, i.e., the cumulative distribution function (CDF) and the complementary CDF (CCDF). To this end, we investigate the three essential components for building surrogates, i.e., types of surrogate models, enrichment methods for experimental designs, and stopping criteria. For each component, we choose several representative methods and study their desirable configurations. In addition, we devise a uniform design (i.e., space-filling design) as a baseline for measuring the improvement of using AL. Combining all the representative methods, a total of 1,920 UQ analyses are carried out to solve 16 benchmark examples. The performance of the selected strategies is evaluated based on accuracy and efficiency. In the context of full distribution estimation, this study concludes that (i) AL techniques cannot provide a systematic improvement compared with uniform designs, (ii) the recommended surrogate modeling methods depend on the features of the problems (especially the local nonlinearity), target accuracy, and computational budget.