While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community,fewer efforts have taken into consideration uncertainties.Those arise from...While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community,fewer efforts have taken into consideration uncertainties.Those arise from a multitude of sources and their quantification and integration in the inversion process are essential in meeting the materials design objectives.The first contribution of this paper is a flexible,fully probabilistic formulation of materials’ optimization problems that accounts for the uncertainty in the process-structure and structure-property linkages and enables the identification of optimal,high-dimensional,process parameters.We employ a probabilistic,data-driven surrogate for the structure-property link which expedites computations and enables handling of non-differential objectives.We couple this with a problem-tailored active learning strategy,i.e.,a self-supervised selection of training data,which significantly improves accuracy while reducing the number of expensive model simulations.We demonstrate its efficacy in optimizing the mechanical and thermal properties of two-phase,random media but envision that its applicability encompasses a wide variety of microstructure-sensitive design problems.展开更多
基金Funded under the Excellence Strategy of the Federal Government and the Länder in the context of the ARTEMIS Innovation Network.
文摘While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community,fewer efforts have taken into consideration uncertainties.Those arise from a multitude of sources and their quantification and integration in the inversion process are essential in meeting the materials design objectives.The first contribution of this paper is a flexible,fully probabilistic formulation of materials’ optimization problems that accounts for the uncertainty in the process-structure and structure-property linkages and enables the identification of optimal,high-dimensional,process parameters.We employ a probabilistic,data-driven surrogate for the structure-property link which expedites computations and enables handling of non-differential objectives.We couple this with a problem-tailored active learning strategy,i.e.,a self-supervised selection of training data,which significantly improves accuracy while reducing the number of expensive model simulations.We demonstrate its efficacy in optimizing the mechanical and thermal properties of two-phase,random media but envision that its applicability encompasses a wide variety of microstructure-sensitive design problems.
