SciML/DiffEqFlux.jl
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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
4 mentions on Hacker News by 3 people: 0 stories, 4 comments · first 2019-11-20 · latest 2020-02-04
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…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-differential-equation [2] https://gist.github.com/ChrisRackauckas/cc6ac746…
comment by ChrisRackauckas · 2020-02-04 · in Standardizing OpenAI’s deep learning framework on PyTorch -
https://github.com/JuliaDiffEq/DiffEqFlux.jl
comment by gardenfelder · 2020-01-14 · in Universal Differential Equations for Scientific Machine Learning -
…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…
comment by ViralBShah · 2019-12-16 · in Jeff Dean interview: Machine learning trends in 2020 -
…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 groups (for…
comment by ChrisRackauckas · 2019-11-20 · in Mixing Differential Equations and Neural Networks for Physics-Informed Learning