A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes

TL;DR

This paper introduces a fully Bayesian, gradient-free dimension reduction method using Gaussian processes that effectively identifies low-dimensional subspaces in high-dimensional, expensive-to-evaluate problems, quantifying uncertainty and outperforming existing methods with limited data.

Contribution

It presents a novel Bayesian approach that does not require gradient information, improving active subspace recovery and predictive accuracy in data-scarce, high-dimensional settings.

Findings

Improves active subspace recovery over existing methods.

Enhances predictive accuracy with limited data.

Provides uncertainty quantification for subspace and predictions.

Abstract

Modern day engineering problems are ubiquitously characterized by sophisticated computer codes that map parameters or inputs to an underlying physical process. In other situations, experimental setups are used to model the physical process in a laboratory, ensuring high precision while being costly in materials and logistics. In both scenarios, only limited amount of data can be generated by querying the expensive information source at a finite number of inputs or designs. This problem is compounded further in the presence of a high-dimensional input space. State-of-the-art parameter space dimension reduction methods, such as active subspace, aim to identify a subspace of the original input space that is sufficient to explain the output response. These methods are restricted by their reliance on gradient evaluations or copious data, making them inadequate to expensive problems without direct access to gradients. The proposed methodology is gradient-free and fully Bayesian, as it quantifies uncertainty in both the low-dimensional subspace and the surrogate model parameters. This enables a full quantification of epistemic uncertainty and robustness to limited data availability. It is validated on multiple datasets from engineering and science and compared to two other state-of-the-art methods based on four aspects: a) recovery of the active subspace, b) deterministic prediction accuracy, c) probabilistic prediction accuracy, and d) training time. The comparison shows that the proposed method improves the active subspace recovery and predictive accuracy, in both the deterministic and probabilistic sense, when only few model observations are available for training, at the cost of increased training time.