Data Fusion with Latent Map Gaussian Processes. (arXiv:2112.02206v2 [stat.ML] UPDATED)

Multi-fidelity modeling and calibration are data fusion tasks that
ubiquitously arise in engineering design. In this paper, we introduce a novel
approach based on latent-map Gaussian processes (LMGPs) that enables efficient
and accurate data fusion. In our approach, we convert data fusion into a latent
space learning problem where the relations among different data sources are
automatically learned. This conversion endows our approach with attractive
advantages such as increased accuracy, reduced costs, flexibility to jointly
fuse any number of data …

arxiv data map ml processes