Yeah, so I think high density informational materials can save one from this feeling. And the measurement of the "interesting degree" it how many information can it provide.
Yeah, It’s true that PDEs are the "top-tier tool" for describing physical phenomena—from the laws of motion in classical mechanics and electromagnetic waves in electromagnetism to the evolution of wave functions in quantum mechanics, they accurately model most macroscopic, classical scenarios. However, when it comes to covering all physical phenomena, they really "fall short": in quantum gravity, spacetime may be discontinuous, making the concept of differentiation meaningless; for complex systems like turbulence, PDEs cannot be solved nor can they capture macroscopic laws; even for the randomness of quantum measurements, PDEs can only predict probability distributions and fail to explain the underlying nature. In short, they are a "top-tier auxiliary," but by no means a "one-size-fits-all key."
Do mean hard to be used because of compute required to render it or because of setting up and understanding the training pipeline and related hyperparameters?
