No, valid question, I just find it difficult to give examples that are easy to understand but let me try. Yes OPs example is not a good one to demonstrate the use case. Let us think about a swarm of drones and their physics, specifically the airflow around them. Hypothetically, you maybe able to describe the physics for a single drone accurately, although this would probably take quite some time in reality. Think days on a simple laptop for a specific configuration if you rally want high accuracy. Nevertheless, if you want to model say 50 drones, things get more complicated. Airflow of one effects the behavior/airflow of others, new turbulence sources and other effects emerge. Actually simulating such a complex system may be infeasible even with supercomputers. Moreover, you are probably interested in many configurations like flight patterns, drone design etc. so that you can choose the best one. In this case, doing a functional interpolation is not very helpful due to the interactions and new emerging effects as we only know the form of the function for a single drone. Sure, you know the underlying equations but you still can't really predict the behavior of the whole without solving them, which is as mentioned costly. The premise of PINNs in this case is to learn to predict the behaviour of this system and the inductive bias is expected to decrease the number of samples required for generalization.