| The `std::autodiff` module in Rust allows differentiable programming: |
| |
| ```rust |
| #![feature(autodiff)] |
| use std::autodiff::*; |
| |
| // f(x) = x * x, f'(x) = 2.0 * x |
| // bar therefore returns (x * x, 2.0 * x) |
| #[autodiff_reverse(bar, Active, Active)] |
| fn foo(x: f32) -> f32 { x * x } |
| |
| fn main() { |
| assert_eq!(bar(3.0, 1.0), (9.0, 6.0)); |
| assert_eq!(bar(4.0, 1.0), (16.0, 8.0)); |
| } |
| ``` |
| |
| The detailed documentation for the `std::autodiff` module is available at [std::autodiff](https://doc.rust-lang.org/std/autodiff/index.html). |
| |
| Differentiable programing is used in various fields like numerical computing, [solid mechanics][ratel], [computational chemistry][molpipx], [fluid dynamics][waterlily] or for Neural Network training via Backpropagation, [ODE solver][diffsol], [differentiable rendering][libigl], [quantum computing][catalyst], and climate simulations. |
| |
| [ratel]: https://gitlab.com/micromorph/ratel |
| [molpipx]: https://arxiv.org/abs/2411.17011v |
| [waterlily]: https://github.com/WaterLily-jl/WaterLily.jl |
| [diffsol]: https://github.com/martinjrobins/diffsol |
| [libigl]: https://github.com/alecjacobson/libigl-enzyme-example?tab=readme-ov-file#run |
| [catalyst]: https://github.com/PennyLaneAI/catalyst |
