A generalised deep learning-based surrogate model for homogenisation utilising material property encoding and physics-based bounds.

The use of surrogate models based on Convolutional Neural Networks (CNN) is increasing significantly in microstructure analysis and property predictions. One of the shortcomings of the existing models is their limitation in feeding the material information. In this context, a simple method is developed for encoding material properties into the microstructure image so that the model learns material information in addition to the structure-property relationship.