# Comparison of Automatic Differentiation (AD)?

**URL:** <https://discourse.julialang.org/t/comparison-of-automatic-differentiation-ad/91544>\
**Category:** Optimization (Mathematical)\
**Created:** [December 12, 2022, 8:20am UTC](https://discourse.julialang.org/t/comparison-of-automatic-differentiation-ad/91544 "2022-12-12T08:20:09Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![nrontsis](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nrontsis/32/38535_2.png) [@nrontsis](https://discourse.julialang.org/u/nrontsis)\
**Post date:** [December 12, 2022, 11:11am UTC](https://discourse.julialang.org/t/comparison-of-automatic-differentiation-ad/91544/2 "2022-12-12T11:11:01Z")

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Related posts:

> **[Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and...](http://www.stochasticlifestyle.com/engineering-trade-offs-in-automatic-differentiation-from-tensorflow-and-pytorch-to-jax-and-julia/)**
>
> To understand the differences between automatic differentiation libraries, let’s talk about the engineering trade-offs that were made. I would personally say that none of these libraries are “better” than another, they simply all make engineering...

> [@Open discussion on the state of differentiable physics in Julia](https://discourse.julialang.org/t/open-discussion-on-the-state-of-differentiable-physics-in-julia/72900/2):
>
> I knew you would ask because you’re hitting THE pain point I know about right now. From the [Universal Differential Equations paper supplement](https://arxiv.org/abs/2001.04385) it describes the 4 vjp choices: Zygote.jl source-to-source transformation based vjps. Note that only non-mutating differential equation function definitions are supported in this mode. This mode is the most efficient in the presence of neural networks. Enzyme.jl source-to-source transformation basd vjps. This is the fastest vjp choice in the presence of …

And [this paper](https://arxiv.org/abs/1812.01892).

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