SciML/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

915 158 +0/wk

GitHub

delay-differential-equations differential-equations differentialequations neural-dde neural-differential-equations neural-jump-diffusions neural-networks neural-ode neural-pde neural-sde neural-sdes ordinary-differential-equations

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Star & Fork Trend (17 data points)

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Multi-Source Signals

GitHub

stars 915

forks 158

Growth Velocity

SciML/DiffEqFlux.jl has +0 stars this period . Velocity data will be available after more historical data is collected.

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Metric	DiffEqFlux.jl	pointer_summarizer	schnetpack	mindnlp
Stars	915	915	914	916
Forks	158	236	251	270
Weekly Growth	+0	+0	+0	+0
Language	Julia	Python	Python	Python
Sources	1	1	1	1
License	MIT	Apache-2.0	NOASSERTION	Apache-2.0

Capability Radar vs pointer_summarizer

DiffEqFlux.jl

pointer_summarizer

Maintenance Activity 99

Last code push 9 days ago.

Community Engagement 86

Fork-to-star ratio: 17.3%. Active community forking and contributing.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 95

Licensed under MIT. Permissive — safe for commercial use.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.