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.
But my daily driver is a system76 Linux box running PopOS… that’s not a swift friendly environment. I guess the best compromise I’ve found for stuff I want to run in both places is Pythonista? Which is fantastic to be clear, but iOS is really constrained…
I want the ML infrastructure of Python, the speed of C and the ability to write fast cross platform apps in a syntax that isn’t confusing violence… Save me programming language nerds.
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.
It solves hyperbolic PDEs like the Euler equations, is differentiable with forward-mode AD, and MPI parallelized.
There are neural networks which learn to produce an approximation, but much faster. Useful for games, maybe exploratory analysis before deploying using slow classic CFD.
Optimizing the bridge only works because my method for solving the forces in each beam (least squares) happens to be differentiable.
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.
