What if we could give any combination of observations of a particular area or event on the Sun along with equations describing the physics to a universal code that creates a physically self-consistent model that explains all the observations? What if we do not need to tell the code how to solve the physics equations? What if the resulting model lives in a space-time continuum without any grid? What if the model could even predict how the Sun evolves after the observations have stopped? What if this inversion science fiction could become reality? In this presentation, I will explain how this could be done using physics-informed neural networks and illustrate it with an extreme inversion problem: deriving velocities, densities, and temperatures as a function of three spatial dimensions and time using only a sequence of continuum images.