Demonstrating a Deep Learning Approach to DEM Inversion

DeepEM is a (supervised) deep learning approach to differential emission measure (DEM) inversion that is currently under development on GitHub.

This first release (cite: https://zenodo.org/record/2587015) coincides with the version of DeepEM demonstrated in Chapter 4 of the Machine Learning, Statistics, and Data Mining for Heliophysics e-book (Bobra & Mason 2018; 10.5281/zenodo.1412824). Within the chapter (and the code provided here, DeepEM.ipynb) we present a demonstration of how a simple implementation of supervised learning can be used to reconstruct DEM maps from SDO/AIA data. Caveats of this simple implementation and future work are also discussed.