The key intuition is calculating the loss function without actually knowing the exact solution ("labels" in supervised learning parlance). Note that this is no

The key intuition is calculating the loss function without actually knowing the exact solution ("labels" in supervised learning parlance). Note that this is not unique to PINN: there are existing numerical methods that do exactly this. I used to solve PDEs for a living; and my academic background is in numerical solutions to PDEs before going into ML. In my industry and academic experience, PINN is a novel curiosity with perhaps niche applications that I am not as familiar with. Yes, I am aware of works of Bruton, Duvenaud et al (and was even in the same lab group with some of them). I am happy to be corrected and learn if PINN has found a strong application. A better introduction to this approach and its critique here: https://arxiv.org/pdf/2206.02016 https://arxiv.org/pdf/2206.02016