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SciML
DiffEqFlux.jl
Julia2019–2023
SciML / DiffEqFlux.jl

DiffEqFlux.jl

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

5Distinct people
6Mentions
0Explicit recommenders
928GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

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

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year6 dated mentions

Observed mentions: 2019-11-20 22:11:06 UTC to 2023-12-04 23:00:13 UTC.

0122019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101

What Hacker News says

6 sources
> In his item #1, he links to https://discourse.julialang.org/t/loaderror-when-using-inter... The issue is actually a Zygote bug, a Julia package for auto-differentiation, and is not directly related to Julia codebase (or Flux package) itself. Furthermore, the problematic code is working fine now, because DiffEqFlux has switched to Enzyme, which doesn't have that bug. He should first confirm whether the problem he is citing is actually a problem or not. > Item #2, again another Zygote bug. If flux chose a buggy package as a dependency, that's on them, and users are well justified in steering clear of Flux if it's authors are not in habit of auditing the depencies they pull in. As of today, the Project.toml for both Flux and DiffEqFlux still lists Zygote as a dependency. Neither list Enzyme. https://github.com/FluxML/Flux.jl/blob/master/Pr
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In Jax vs. Julia (Vs PyTorch)
At this point, the DiffEqFlux neural differential equation library fits the neural ODE example from the original paper in 29 seconds [1]. The forward pass of torchdiffeq on trivial ODEs without neural networks takes 47 seconds [2] (and of course adding in neural networks makes it a lot more expensive). This is a massive real-world difference. It means that the Julia packages are building animations by looking at real-time fitting plots, while it's a hours long ordeal in PyTorch. Being able to use optimized packages instead of hardcoding a simple version of things really pays off in the long run, and here using a real ODE solver suite is not a small difference but rather it's multiple orders of magnitude. That's the real benefit of differentiable programming. [1] https://github.com/JuliaDiffEq/DiffEqFlux.jl#training-a-neural-ordinary-dif
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In Standardizing OpenAI’s deep learning framework on PyTorch
I am happy to share my perspective on this. I feel a really good blog on this topic is Bret Victor's blog post - What can a technologist do about climate change? http://worrydream.com/ClimateChange/ One of the clear recommendations is - contribute to Julia. A lot of what Bret said in 2015 has actually panned out. Julia has become a powerful language for scientific computing and machine learning. As a result it is being used in climate projects such as Climate Machine (MIT and Caltech). The Julia Lab at MIT participates in this project: Climate Machine: https://clima.caltech.edu/ Github: https://github.com/climate-machine/ Julia Lab: http://julia.mit.edu/ Contributions to Julia packages (compiler, stdlibs, math packages, ML packages, parallel computing) will end up finding their way into climate research, because of the extensive reuse of
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code within the Julia ecosystem. Specifically capabilities such as Zygote.jl (https://github.com/FluxML/Zygote.jl) for differentiable programming have the potential to dramatically make it easy to apply ML techniques to scientific codebases. Compiler contributors are hard to come along, so all contributions to compiler technology are incredibly valuable. The DiffEqFlux.jl ecosystem in Julia is a good example of combining mechanistic models with ML (https://github.com/JuliaDiffEq/DiffEqFlux.jl). Hop on to the Julia slack channel or discourse to dig in deeper. Another thing Bret Victor speaks about in his blog post is working with agencies such as ARPA-E on advanced projects. Julia Computing is participating in an ARPA-E project to bring these capabilities to many energy related simulation and development technologies. This press release gives a broad idea: ARPA-E press release: https://www.energy.gov/articles/department-energy-announces-... Funded projects: https://arpa-e.energy.gov/sites/default/files/documents/file... Julia Computing press release: https://juliacomputing.com/communication/2019/12/09/arpa-e.h...
In Jeff Dean interview: Machine learning trends in 2020
>In the later section, where you introduce the Physics Informed Neural Network, it seems (but I could be misunderstanding) that you are assuming I have measurements of the actual solution and we're training the network with that data. Is this correct? Yes, this is the PINN method M.Raissi, P.Perdikaris, and G.E.Karniadakis: use a physical underpinning as a function to help the learning but relax towards the data. >I was initially imagining a method where say I know the solution to the linear part of the differential equation and then train the network just using the non-linear part without having to have any data. These are the mixed neural differential equations that I have been showcasing with DiffEqFlux.jl https://github.com/JuliaDiffEq/DiffEqFlux.jl#mixed-neural-de... So it's just different methods by different people / different grou
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ps (for different purposes).
In Mixing Differential Equations and Neural Networks for Physics-Informed Learning

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