Though the use of machine learning (ML) is ubiquitous in astrophysics and cosmology, many still see the opacity of ML algorithms as a major issue to their scientific utility. One way of addressing this opacity is through an emerging trend in ML research: “teaching” ML algorithms physical laws and domain-specific knowledge. “Physics-informed machine learning” (PIML), as this methodology is called, promises to produce better predictions and yield more interpretable algorithms. It does so by using physical principles in the training process and/or by using physical principles to guide the development of the neural network architecture. In this chapter, I investigate two uses of PIML in astronomy/cosmology, each a representative example of the two PIML methods. In both cases, PIML provides improvements in terms of the predictions and efficiency of ML algorithms. However, I argue that only in the second case does PIML offer improvements to the interpretability of the algorithms.