Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks.

ABSTRACT: Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena. Neural nets are trained by self-supervised learning with image sequences from simulations of several common processes, including plane-wave propagation, grain growth, spinodal decomposition, and dendritic crystal growth. The trained networks can accurately predict both short-term local dynamics and long-term statistical

SUBMITTER: Yang K 

PROVIDER: S-EPMC8134942 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature