+SciML is huge. If I say "I am using a SciML package", that could mean DifferentialEquations.jl, but it could also mean NonlinearSolve.jl, or ExponentialUtilities.jl, MuladdMacro.jl, or anything in the long tail. Yet it is treated, maintained, and documented as a cohesive whole. But in terms of maturity, that is definitely not the case. This talk will highlight and put firm grading on the maturity of different parts of the project, where pieces like the ODE solvers are highly mature while other aspects like GPU-based optimizers or high index DAEs have a medium level of maturity, while other promising and popular libraries such as MethodOfLines.jl have a lot Discourse discussion but are knowingly at an immature stage. Part of this talk is to paint in broad strokes a picture of the current state of the ecosystem to help the general user base better understand the current state of the project.
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