Abstract Scope |
Computational thermodynamics, including the calculation of phase diagrams approach (CALPHAD), is an essential tool in the design and deployment of materials for extreme environments. CALPHAD databases span an enormous compositional space, but data is sparse, leading to potentially unreliable predictions for multicomponent materials. In recent years, researchers have begun to prioritize the development of uncertainty quantification approaches for CALPHAD modeling to provide useful uncertainties. In this presentation we share recent developments in Bayesian methods for parameter inference, model selection, uncertainty quantification and propagation, and data weighting, with examples ranging from binary alloys to the high temperature processing of ceramic materials. |