In the process of “shut up and calculate” (SUAC) you often come upon much deeper insights about the involved processes and quantities than you ever could with wordy and “understandable” explanations. Of course simple, explainable models are great, but they have limited ability to describe the world, which is much more complicated. I don’t see how successfully handling this complexity (and getting the successful predictions) could take away from understanding.
It’s not like those SUAC disciplines don’t have simple explainable models. In my opinion It’s just that in order to describe phenomena really accurately or if the described phenomena are complicated enough the only way to get reliable and improvable predictions is via complicated SUAC-type calculations.
My point is not that there are no insights coming from SUAC, but rather that explanations are not a required thing of science, because predictions work without them, and the accuracy of predictions, and making new predictions makes science move forward, whether with explanations or without.
It’s not like those SUAC disciplines don’t have simple explainable models. In my opinion It’s just that in order to describe phenomena really accurately or if the described phenomena are complicated enough the only way to get reliable and improvable predictions is via complicated SUAC-type calculations.