You can use Pytorch for more than just Neural Networks - its autograd is super powerful for any problem where you need gradients (and are too lazy to calculate them yourself...)!
Author here. Yep, obviously this is more commonly done with dedicated optimization libraries, but the fun part was doing it in Pytorch to use autograd and as a way to visualize optimizers etc :)
I forgot the name of the software but when I took statics way back in the day we used an app that let us design the bridge and then it animated a truck going across. It was cute but it also highlighted (literally) all the areas that were in stress during crossing. I enjoyed that class - it was interesting even if it had nothing to do with programming. :)
I remember several years ago when differentiable programming was an object of interest to the programming community and Lattner was trying to make Swift for Tensorflow happen[1].<p>I'm of the opinion that it was ahead of its time: Swift hadn't (and still hasn't) made enough progress on Linux support for it to be taken seriously as a language for writing anything that isn't associated with Apple. However, as a result, Swift now has language-level differentiability in its compiler. I'd love to see Swift get used for projects like this, but I suppose the reality of the matter is that there are so many performant runtimes for 2D/3D physics that there just isn't much of a need for automatic differentiation (and its overhead) to solve these problems. The tooling nerd in me thinks this stuff is fascinating.<p><a href="https://github.com/tensorflow/swift">https://github.com/tensorflow/swift</a>
> You can use Pytorch for more than just Neural Networks<p>Facts. Pytorch is such a fun too for applied calculus. Just write down a program, compute its derivative, and do any of the fun things you can do with derivatives, like optimization or linear approximation.
I want someone to do this for a fluid dynamics problem.<p>I suspect lots of things in everyday life could be made substantially better/cheaper/more efficient if entire system optimization like this could be done to their design.