AFAIK, It's mainly used for implementing gradient descent, which is used for training neural networks.
Frameworks like pytorch, tensorflow, probably used back propagation to calculate the gradient of a multidimensional function. But in involves tracing, and storing the network state during the forward pass.
Static automatic differentiation should be faster and should look a lot like differentiation is done mathematically rather than numerically.
Of course there are more applications to AD in scientific computing.
Frameworks like pytorch, tensorflow, probably used back propagation to calculate the gradient of a multidimensional function. But in involves tracing, and storing the network state during the forward pass.
Static automatic differentiation should be faster and should look a lot like differentiation is done mathematically rather than numerically.
Of course there are more applications to AD in scientific computing.