3D µCT images of specimens of carbon fiber reinforced polyamide 6 plaque, fiber orientation tensor data of these images, and three Python code files for two different algebraic and one machine learning based tensor interpolation algorithms

Most importantly, this dataset contains three different Python codes. The author implemented a different interpolation method in each of those codes; two algebraic and one machine learning based one. The component averaging method is the simplest; the decomposition method is mathematically more difficult.

It works with the decomposition of the tensor into shape and orientation and subsequent separate invariant and quaternion weighting, before reassembling the then interpolated tensor. The deep learning based method is the only Jupyter notebook in this dataset, where an ANN is implemented for the same interpolation task. Please consider the reference paper mentioned before for details.

For the visualization of the tensor glyphs, a Matlab function by Barmpoutis is used, which can be found here: de.mathworks.com/matlabcentral/fileexchange/27462-diffusion-tensor-field-dti-visualization.