Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
This addresses the challenge for theoretical physicists in balancing empirical fitting with aesthetic principles, but it is incremental as it applies existing machine learning techniques to a specific domain.
The paper tackled the problem of determining model parameters in theoretical physics by fitting to experimental data and satisfying abstract criteria like beauty, using the Yukawa quark sector as a toy example, and demonstrated that minimizing proposed loss functions results in true models that are also beautiful as measured by uniformity, sparsity, or symmetry criteria.
Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example to demonstrate how both of those tasks can be accomplished with machine learning techniques. We propose loss functions whose minimization results in true models that are also beautiful as measured by three different criteria - uniformity, sparsity, or symmetry.