Gene

Engineering

Philosophy

Multiphysics

Ecology

Biology

Contrast (vision)

Materials science

Biochemistry

Computer science

Linkage (software)

Homogenization (climate)

Mechanical engineering

Finite element method

Epistemology

Artificial intelligence

Composite material

Structural engineering

Chemistry

Deep learning

Stiffness

Biodiversity

Property (philosophy)

Machine learning

Computational Materials Science

Data-driven methods are emerging as an important toolset in the studies of multiscale, multiphysics, materials phenomena. More specifically, data mining and machine learning methods offer an efficient toolset for extracting and curating the important correlations controlling these multiscale materials phenomena in high-value reduced-order forms called process-structure-property (PSP) linkages. Traditional machine learning methods usually depend on intensive feature engineering, and have enjoyed some success in establishing the desired PSP linkages. In contrast, deep learning approaches provide a feature-engineering-free framework with high learning capability. In this work, a deep learning approach is designed and implemented to model an elastic homogenization structure-property linkage in a high contrast composite material system. More specifically, the proposed deep learning model is employed to capture the nonlinear mapping between the three-dimensional material microstructure and its macroscale (effective) stiffness. It is demonstrated that this end-to-end framework can predict the effective stiffness of high contrast elastic composites with a wide of range of microstructures, while exhibiting high accuracy and low computational cost for new evaluations.

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets