With a few lines of Python code, it is possible to train a neural network over a data-set and then use it to make extrapolations.
These techniques are routinely used in particle physics and condensed matter and only recently some pioneering work has been done to apply them to the nuclear physics case.
Given the incredible potential of these techniques, is it still necessary to invest time to build complex nuclear models to do the same thing?
Why not directly use machine learning algorithms to analyse the data?

In my talk, I will present some applications of machine learning techniques to the case of nuclear masses: by using either neural networks [1] or Gaussian Process Emulators [2] I will show how to use these algorithms to reproduce this particular observable. In particular I will consider the case of a neural network to reproduce nuclear masses without any underlying model and with a model to improve performances.

This procedure may be very helpful: in the short term, it will help us detect possible trends in the data and eventually perform reliable predictions in nearby regions of the nuclear chart; in the long term, by interpreting the algorithms, we may learn what is the missing physics in the nuclear model we currently use.

[1] Pastore, A., & Carnini, M. (2021). Extrapolating from neural network models: a cautionary tale. Journal of Physics G: Nuclear and Particle Physics
[2]Shelley, M., & Pastore, A. (2021). A new mass model for nuclear astrophysics: crossing 200 keV accuracy. Universe, 7(5), 131