[HN Gopher] Designing bridge trusses with Pytorch autograd
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       Designing bridge trusses with Pytorch autograd
        
       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 : eschluntz
       Score  : 22 points
       Date   : 2024-01-11 20:20 UTC (2 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | eschluntz wrote:
       | 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 :)
        
       | dwrodri wrote:
       | 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].
       | 
       | 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.
       | 
       | https://github.com/tensorflow/swift
        
       | fritzo wrote:
       | > You can use Pytorch for more than just Neural Networks
       | 
       | 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.
        
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       (page generated 2024-01-11 23:00 UTC)