Gradient-enhanced deep Gaussian processes for multifidelity modelling

arXiv:2402.16059v1 Announce Type: cross
Abstract: Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process. Dense low-fidelity samples are used to reduce interpolation error, while sparse high-fidelity samples are used to compensate for bias or noise in the low-fidelity samples. Deep Gaussian processes (GPs) are attractive for multifidelity modelling as they are non-parametric, robust to overfitting, perform well for small datasets, and, critically, can capture nonlinear and input-dependent relationships between data of different fidelities. …

abstract arxiv bias cs.lg data error fidelity gaussian processes gps gradient low modelling multiple noise process processes reduce samples stat.ml type