>The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estim

>The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time. This is pretty fresh tech, but industry is already using it. We are doing something approximately similar where I work. Instead of running compute intensive finite element/finite difference simulations of physically dependent systems, a neural network (typically something structured like a transformer) is trained to output the calculations up to 6 orders of magnitude faster in our applications. This changes this allows modeling dependent science and engineering solutions to be iterated over in real time - you can see the results of your edits as you manipulate your models. And the results, at least in our applications, have some ≈98 MSE. It isn't surprising in hindsight - deep neural networks are universal function approximations, and finite modeling is as close to pure mathematics as you can get in an industry setting. It feels like a perfect use case for deep neural nets.