  Stochastic microstructure reconstruction involves digital generation of
microstructures that match key statistics and characteristics of a (set of)
target microstructure(s). This process enables computational analyses on
ensembles of microstructures without having to perform exhaustive and costly
experimental characterizations. Statistical functions-based and deep
learning-based methods are among the stochastic microstructure reconstruction
approaches applicable to a wide range of material systems. In this paper, we
integrate statistical descriptors as well as feature maps from a pre-trained
deep neural network into an overall loss function for an optimization based
reconstruction procedure. This helps us to achieve significant computational
efficiency in reconstructing microstructures that retain the critically
important physical properties of the target microstructure. A numerical example
for the microstructure reconstruction of bi-phase random porous ceramic
material demonstrates the efficiency of the proposed methodology. We further
perform a detailed finite element analysis (FEA) of the reconstructed
microstructures to calculate effective elastic modulus, effective thermal
conductivity and effective hydraulic conductivity, in order to analyse the
algorithm's capacity to capture the variability of these material properties
with respect to those of the target microstructure. This method provides an
economic, efficient and easy-to-use approach for reconstructing random
multiphase materials in 2D which has the potential to be extended to 3D
structures.
