HAO Colloquium - Andrés Asensio Ramos, IAC

Inverse problems in Solar Physics via Deep Learning

In the last decade, machine learning has experienced an enormous advance, thanks to the possibility to train very deep and complex neural networks. In this contribution I show how we are leveraging deep learning to solve difficult problems in Solar Physics. I will focus on how differentiable programming (aka deep learning) is helping us to have access to velocity fields in the solar atmosphere, correct for the atmospheric degradation of spectropolarimetric data and carry out fast 3D inversions of the Stokes parameters to get physical information of the solar atmosphere.

Date and time: 
Tuesday, February 18, 2020 - 2:00pm to 3:00pm
Building: 
CG-1
Room: 
2139 - Capt. Mary