Is there academic research on how to choose the number of layers and neurons in a neural network? I did some research and I understand that there is no right answer in choosing the number of layers and neurons in a neural network architecture. I have seen some guidelines and rules of thumb in posts and blogs. Even though they are helpful, I am wondering if there is any academic paper on them.
Do you have any study/work/research about how to choose the number of layers and neurons to share?

Often more neurons/layers than needed are chosen, and then regularization techniques are used to get a lower effective complexity than the potential complexity. But even then, there is not a canonical answer to how many neurons/layers to choose.
Source: Xia Hu et al. "Model Complexity of Deep Learning: A Survey." (2021).
