# SciML/DiffEqFlux.jl

<https://repo.masters-masters.com/en/repos/SciML/DiffEqFlux.jl>

[Home](https://repo.masters-masters.com/en) › [SciML](https://repo.masters-masters.com/en/owners/SciML) › [DiffEqFlux.jl](https://repo.masters-masters.com/en/repos/SciML/DiffEqFlux.jl)

SciML/DiffEqFlux.jl has 928 stars on GitHub. Its RepoMasters card: Beta tier. Pre-built implicit layer architectures with O(1) backprop, GPUs, and…

| Field | Value |
| --- | --- |
| Owner | [SciML](https://repo.masters-masters.com/en/owners/SciML) |
| Stars | 928 |
| Forks | 162 |
| Language | [Julia](https://repo.masters-masters.com/en/languages/julia) |
| License | MIT |
| Created | 2018 |
| Topics | neural-ode, neural-sde, neural-pde, neural-dde, neural-differential-equations, stiff-ode, ordinary-differential-equations, stochastic-differential-equations, delay-differential-equations, partial-differential-equations |
| Tier | [Beta (700 to 1,999 ★)](https://repo.masters-masters.com/en/ranks/box_office) |
| View on GitHub | [SciML/DiffEqFlux.jl](https://github.com/SciML/DiffEqFlux.jl) |

## Repository

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

The tier for repos with 700 to 1,999 ★. Published odds per card pulled: 6%. Rarity: Rare.

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Data: GitHub · Illustration: the project’s public image, pixelated by RepoMasters.