  The microstructure analyses of porous media have considerable research value
for the study of macroscopic properties. As the premise of conducting these
analyses, the accurate reconstruction of microstructure digital model is also
an important component of the research. Computational reconstruction algorithms
of microstructure have attracted much attention due to their low cost and
excellent performance. However, it is still a challenge for computational
reconstruction algorithms to achieve faster and more efficient reconstruction.
The bottleneck lies in computational reconstruction algorithms, they are either
too slow (traditional reconstruction algorithms) or not flexible to the
training process (deep learning reconstruction algorithms). To address these
limitations, we proposed a fast and flexible computational reconstruction
algorithm, neural networks based on improved simulated annealing framework
(ISAF-NN). The proposed algorithm is flexible and can complete training and
reconstruction in a short time with only one two-dimensional image. By
adjusting the size of input, it can also achieve reconstruction of arbitrary
size. Finally, the proposed algorithm is experimentally performed on a variety
of isotropic and anisotropic materials to verify the effectiveness and
generalization.
