I’m trying to find the derivative in ModelingToolkit.jl
This is how it was done in SymEngine
using SymEngine
x=symbols(:x)
diff(4x^3,x)
would give 3x^2
How would I do the same thing in ModelingToolkit?
I’m trying to find the derivative in ModelingToolkit.jl
This is how it was done in SymEngine
using SymEngine
x=symbols(:x)
diff(4x^3,x)
would give 3x^2
How would I do the same thing in ModelingToolkit?
See the first tutorial: https://mtk.sciml.ai/dev/tutorials/symbolic_functions/#Derivatives-1
Ok thanks I have it working one thing I noticed
using Latexify
using ModelingToolkit
@variables x
@derivatives δx'~ x
F(x)=(3x^2+x^3)/x
latexify(expand_derivative(δx(F)))
produces an output containing the factor (x^-2) instead of dividing everything by (x^2), which is hardely an issue, except that it won’t latexify as a function.
I’m also not finding that simplification does much.
This expression should reduce to (2x+3).
Please open an issue with simplifications you see fail. That is all about tweaking the heuristics, or training a neural network.
I put an issue on GitHub. If it’s for AI I can do it with every problem that isn’t working.
Right now it’s rule-based, but next summer I hope we start training reinforcement learning and deploy it as one of the simplify options. Some papers suggest it can generally due better if you train it long enough
I can see that it would. I’d think most of the ones that exist are rule based, but it should be interesting to see how much gets together. I might offer to help if I have time to learn actual coding.