Polynomial Chaos Expanded Gaussian Process

arXiv:2405.01052v1 Announce Type: new
Abstract: In complex and unknown processes, global models are initially generated over the entire experimental space, but they often fail to provide accurate predictions in local areas. Recognizing this limitation, this study addresses the need for models that effectively represent both global and local experimental spaces. It introduces a novel machine learning (ML) approach: Polynomial Chaos Expanded Gaussian Process (PCEGP), leveraging polynomial chaos expansion (PCE) to calculate input-dependent hyperparameters of the Gaussian process (GP). This approach …

abstract arxiv chaos cs.lg cs.sy eess.sy experimental generated global machine novel polynomial predictions process processes space spaces study type