neuron-explorer
Steering-first neuron explorer for Qwen3.5-4B: push every MLP neuron, keep every rollout, browse the results
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
Developer toolsJuliaGitHub @SciMLSelf-installOwner unconfirmed
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DiffEq(For)Lux.jl (aka DiffEqFlux.jl) fuses the world of differential equations with machine learning
by helping users put diffeq solvers into neural networks. This package utilizes
DifferentialEquations.jl, and Lux.jl as its building blocks to support research in
Scientific Machine Learning, specifically neural differential equations to add physical information into traditional machine learning.
We maintain backwards compatibility with Flux.jl via FromFluxAdaptor()Read the full README ↗The full README is unavailable right now. Read it at the original link. The saved excerpt is shown below.
DiffEq(For)Lux.jl (aka DiffEqFlux.jl) fuses the world of differential equations with machine learning
by helping users put diffeq solvers into neural networks. This package utilizes
DifferentialEquations.jl, and Lux.jl as its building blocks to support research in
Scientific Machine Learning, specifically neural differential equations to add physical information into traditional machine learning.
We maintain backwards compatibility with Flux.jl via FromFluxAdaptor()Read the full README ↗