Abstract Scope |
Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this presentation, we demonstrate how to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a computational model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs. To solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures to define a unique and numerically stable solution. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning surrogate models and demonstrate the proposed methodology on two representative case studies in structure–property linkages. |