The abilities of artificial neural networks and Gaussian processes to model the yield strength of nickel-base superalloys as a function of composition and temperature have been compared on the basis of simple well-known metallurgical trends (influence of Ti, Al, Co, Mo, W, Ta, of the Ti/Al ratio, γ' volume fraction and the testing temperature). The methodologies are found to give similar results, and are able to predict the behaviour of materials that were not shown to the models during their creation. The Gaussian process modelling method is the simpler method to use, but its computational cost becomes larger than that of neural networks for large data sets.
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Tancret et al. (1999) studied this question.