Constrained Gaussian Process with Application in Tissue-engineering Scaffold Biodegradation

In many biomanufacturing areas such as tissue-engineering scaffold fabrication, the biodegradation performance of products is a key to produce products with desirable properties. Prediction of biodegradation often encounters the challenge on how to incorporate expert knowledge appropriately. This paper proposes a constrained Gaussian process (CGP) method for predictive modeling with application to scaffold biodegradation. It provides a unified framework of using proper constraints to accommodate various types of expert knowledge in predictive modeling, including censoring, monotonicity, and bounds requirements. Efficient Bayesian sampling procedures for prediction are also developed. The performance of the proposed method is demonstrated in a case study from a novel scaffold fabrication process. Compared with the unconstrained GP and artificial neural networks, the D ow nl oa de d by [ U ni ve rs ity L ib ra ri es | V ir gi ni a T ec h] a t 1 3: 17 1 8 D ec em be r 20 17