[fault1]

It depends on what you want to do obviously.

pytorch historically hasn't really focused on forward mode auto differentiation: https://github.com/pytorch/pytorch/issues/10223

this definitely limits its generality relative to jax, which makes it less than ideal for anything other than 'typical' deep neural networks

this is especially true when the research in question is related to things like physics or combining physical models and machine learning, which imho is very interesting. those are use cases that pytorch just isn't good at.

[SleekEagle]

Interesting - I didn't realize that it was that important for computational physics. Very cool, I'll have to read up!