Dear all,

I wanted to make you aware of an excellent resource that is currently being compiled by Monica Bobra and James Mason: the HelioML e-book (https://helioml.github.io/HelioML/chapters/title). 

We have recently contributed a chapter based on DeepEM: a (supervised) deep learning approach to DEM inversion. DeepEM is currently under development on Github (https://github.com/PaulJWright/DeepEM), and DeepEM has been designed to both accelerate the computation of DEM solutions, and reduce erroneous solutions provided by pre-existing inversion methods. The underlying idea is that a neural network should be able to learn a generalized mapping in order to transform an SDO/AIA input vector (6x1x1) to the DEM output vector (Nx1x1, where N is the number of temperature bins of the DEM solution) also retain the fidelity to the original inversion method.

In our DeepEM chapter we provide a simple model in order to demonstrate how easy it is to apply deep learning techniques to your own research. This very simple implementation is trained on one set of 512x512 AIA images, and corresponding Basis Pursuit DEM solutions. This implementation returns >10 Million DEM/s with similar fidelity to Basis Pursuit. A gif of how this implementation compares to Basis Pursuit is shown here: https://twitter.com/PaulJWright/status/1068115587614863360, and I hope that this chapter, and e-book in general, will encourage those who are interested to applying deep learning to their research.

Cheers,
Paul
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Paul J. Wright
Research Assistant & Ph.D. Student

Astronomy & Astrophysics Group
SUPA School of Physics & Astronomy
Kelvin Building
University of Glasgow
Glasgow, UK
G12 8QQ