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Abstract Near the end of their lives, low-mass and intermediate-mass stars expel metal-enriched material in winds and outflows, ultimately producing planetary nebulae1,2. The ejected material is expected to fragment and mix into the interstellar medium (ISM), but this final assimilation step has been difficult to observe directly3,4.
Machine learning is bursting with potential applications, but one important (and simple!) usage is using a machine learning algorithm for interpolation. In this post, I will demonstrate how a simple neural network can be used to interpolate on 1D or 2D grids. This is of particular interest for me in interpolating large vectors on a 2D spatial grid. 1-D interpolation asks the simple question of given an x value predict a y value.
In January 2020, the COVID-19 pandemic rocked the world by sending many countries across the globe into lockdown and curfews and instilling a general sense of fear of the viral infection in every neighborhood. While the repercussions of this event are still being felt in everyday life, we collectively have started to move past it.
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