Frequently, you still need a clever subject matter expert to figure out what form of input you need to insert into the neural network. E.G. someone has to figu

Frequently, you still need a clever subject matter expert to figure out what form of input you need to insert into the neural network. E.G. someone has to figure out how to take crystal structure and put it in a neural network. Most obvious choices (e.g. voxel models) are intractable without huge amounts of computational resources which aren't generally available Your data needs to be very cheap. And you don't have to be that clever. For periodic function sines and cosines. For potential energy quadratics, polynomials and exponents do a great job. One of the key components which makes neural networks generally inferior is that you need enough data to learn the governing equations (always true physics) as well as the constitutive law (part of the physics which applies only to your problem) as opposed to just the constitutive law. Of course there are notable exeptions. Neural networks have been doing a great job of processing microscopy data and building 3d models from a series of 2d images.