  Combining physics with machine learning models has advanced the performance
of machine learning models in many different applications. In this paper, we
evaluate adding a weak physics constraint, i.e., a physics-based empirical
relationship, to the loss function (the Physics Informed manner) in local
distance explosion discrimination in the hope of improving the generalization
capability of the machine learning model. We compare the proposed model to the
two-branch model we previously developed, as well as to a pure data-driven
model. Unexpectedly, the proposed model did not consistently outperform the
pure data-driven model. By varying the level of inconsistency in the training
data, we find this approach is modulated by the strength of the physics
relationship. This result has important implications for how to best
incorporate physical constraints in Machine Learning models.
