  The discovery process of building new theoretical physics models involves the
dual aspect of both fitting to the existing experimental data and satisfying
abstract theorists' criteria like beauty, naturalness, etc. We design loss
functions for performing both of those tasks with machine learning techniques.
We use the Yukawa quark sector as a toy example to demonstrate that the
optimization of these loss functions results in true and beautiful models.
