# How to tackle 2D reaction-diffusion equation?

**URL:** <https://discourse.julialang.org/t/how-to-tackle-2d-reaction-diffusion-equation/54110>\
**Category:** Numerics\
**Created:** [January 28, 2021, 10:41am UTC](https://discourse.julialang.org/t/how-to-tackle-2d-reaction-diffusion-equation/54110 "2021-01-28T10:41:04Z")\
**Posts on this page:** 1\
**Showing post:** 7

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**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [January 28, 2021, 6:26pm UTC](https://discourse.julialang.org/t/how-to-tackle-2d-reaction-diffusion-equation/54110/7 "2021-01-28T18:26:42Z")

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Also if you’re new to Julia, the following has a tutorial on optimizing differential equation code, where the big example is a 2D reaction-diffusion. It uses different tricks (no sparsity tricks shown here), and focuses more on the code optimization.

[https://tutorials.sciml.ai/html/introduction/03-optimizing\_diffeq\_code.html](https://tutorials.sciml.ai/html/introduction/03-optimizing_diffeq_code.html)

This blog post focuses on GPUs:

> **[Solving Systems of Stochastic PDEs and using GPUs in Julia - Stochastic...](https://www.stochasticlifestyle.com/solving-systems-stochastic-pdes-using-gpus-julia/)**
>
> What I want to describe in this post is how to solve stochastic PDEs in Julia using GPU parallelism. I will go from start to finish, describing how to use the type-genericness of the DifferentialEquations.jl library in order to write a code that uses...

And the MTK automatic optimization and parallelism tools all showcase 2D reaction diffusion.

[https://mtk.sciml.ai/dev/tutorials/auto\_parallel/](https://mtk.sciml.ai/dev/tutorials/auto_parallel/)

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