Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit

TL;DR

This paper introduces GUESS, a new adaptive sampling strategy for surrogate modeling that combines exploration and exploitation, achieving higher sample efficiency than existing methods in benchmark tests.

Contribution

The paper presents GUESS, a novel sampling strategy that integrates uncertainty and gradient information, improving global fit accuracy with fewer samples.

Findings

GUESS outperforms 9 other strategies in sample efficiency.

It is effective across 26 benchmark functions.

Higher-dimensional performance and surrogate choice impact are analyzed.

Abstract

Surrogate models based on machine learning methods have become an important part of modern engineering to replace costly computer simulations. The data used for creating a surrogate model are essential for the model accuracy and often restricted due to cost and time constraints. Adaptive sampling strategies have been shown to reduce the number of samples needed to create an accurate model. This paper proposes a new sampling strategy for global fit called Gradient and Uncertainty Enhanced Sequential Sampling (GUESS). The acquisition function uses two terms: the predictive posterior uncertainty of the surrogate model for exploration of unseen regions and a weighted approximation of the second and higher-order Taylor expansion values for exploitation. Although various sampling strategies have been proposed so far, the selection of a suitable method is not trivial. Therefore, we compared our proposed strategy to 9 adaptive sampling strategies for global surrogate modeling, based on 26 different 1 to 8-dimensional deterministic benchmarks functions. Results show that GUESS achieved on average the highest sample efficiency compared to other surrogate-based strategies on the tested examples. An ablation study considering the behavior of GUESS in higher dimensions and the importance of surrogate choice is also presented.