Efficient Gaussian Process Modeling for Replicated and Censored Data in Manufacturing Applications

Music City Center 

Gaussian process (GP) regression is widely used for modeling responses in both physical and computer experiments. In practice, data from physical experiments may include replications and be subject to censoring due to equipment limitations or contextual constraints. Motivated by real-world manufacturing data, we develop a GP modeling framework that efficiently analyzes replicated and potentially censored data. Our approach leverages recent advances in likelihood-based inference and latent-variable methods for GPs, effectively exploiting replication while rigorously incorporating censoring information-analogous to exact simulation techniques for truncated distributions. We demonstrate the effectiveness of our method on synthetic manufacturing data, showing that it provides enhanced prediction, uncertainty quantification, and computational scalability, making it well-suited for large-scale applications with structured replication and incomplete data.

Censored data

Gaussian process

Replication

Truncated multivariate normal 

Section on Physical and Engineering Sciences