Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning

TL;DR

This paper introduces deepICMGP, a novel deep Gaussian process model for multi-output surrogate modeling that captures complex dependencies and incorporates active learning for efficient experimental design.

Contribution

It extends the Intrinsic Coregionalization Model into a deep hierarchical structure for multi-output Gaussian processes, enabling better dependency modeling and active learning integration.

Findings

DeepICMGP outperforms existing multi-output GPs in benchmarks.

Active learning improves input selection efficiency.

Hierarchical coregionalization captures complex output dependencies.

Abstract

Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings remains challenging due to the need for efficient dependency modeling. We propose the Deep Intrinsic Coregionalization Multi-Output Gaussian Process (deepICMGP) surrogate for computer simulation experiments involving multiple outputs, which extends the Intrinsic Coregionalization Model (ICM) by introducing hierarchical coregionalization structures across layers. This enables deepICMGP to effectively model nonlinear and structured dependencies between multiple outputs, addressing key limitations of traditional multi-output GPs. We benchmark deepICMGP against state-of-the-art models, demonstrating its competitive performance. Furthermore, we incorporate active learning strategies into deepICMGP to optimize sequential design tasks, enhancing its ability to efficiently select informative input locations for multi-output systems.