Hierarchical Bayesian approach to experimental data fusion: Application to strength prediction of high entropy alloys from hardness measurements

Abstract

The discovery of materials with improved properties can be accelerated by models with the ability to combine data from multiple experimental information sources. A recurring task in the toolbox of practitioners is to map input physical descriptors to output properties of interest. Typically, both the outputs and many of the inputs are experimentally measured and, thus, noisy. Probabilistic regression methods, e.g., Gaussian process regression, can easily deal with noisy outputs, even if the noise is input-dependent. However, most regression methods cannot process noisy inputs. Ignoring input uncertainty leads to inaccurate predictive uncertainty, a crucial ingredient for the sequential design of experiments. The objective of this paper is to develop a regression methodology that can deal with input uncertainty when one wishes to correlate an inexpensive experimental measurement (e.g., hardness) to an expensive one (e.g., yield strength). Our hierarchical Bayesian approach uses two Gaussian processes. The first one maps noiseless physical descriptors to the inexpensive experimental measurement. The second Gaussian process maps noiseless physical descriptors and the inexpensive experimental measurement to the expensive experimental measurement. The two Gaussian processes form a nested model that is not analytically tractable. To overcome this issue, we propose semi-analytical approximations to both the marginal likelihood and the posterior predictive distribution. The result is a model that is practical to train and use. We demonstrate the merits of the proposed method through a synthetic dataset in which we control all the uncertainties. The statistical tests clearly show that standard Gaussian process regression cannot cope with input uncertainty whereas our proposed method consistently yields better predictive distributions. Finally, we apply the method to the task of predicting the yield strength of high entropy alloys from hardness on an exhaustive dataset compiled from the available literature.