I’ve only been playing with MTK.jl for a day or two, but I’m really impressed - the workflow here is so much nicer than using a dedicated acausal modelling language! I’m running into situations where judicious function registration is advisable, and I’d like to understand how flexibly derivatives ca…

i think you need to generate a wrapper function around the ForwardDiff.derivative function and use that instead: AD_derivative(f,x) = ForwardDiff.derivative(f,x) foo_f3(x) = x^2 @register_symbolic AD_derivative(f::Function,x) @register_symbolic foo_3(x) Symbolics.derivative(::typeof(foo_f3), args::…

Using ForwardDiff for Symbolic.derivative() definition

Specific Domains Modelling & Simulations

ChrisRackauckas November 25, 2024, 1:39pm 2

dfdt_3 = Symbolics.derivative(foo_f3(t), t) ?

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Using ForwardDiff for Symbolic.derivative() definition

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