JTaets
JTaets
Since working some more with ModelingToolkit, I would like to express my opinion on this. I think that `D(x) ~ 0` is still best, having discrete variables in `prob.p` seems...
I like it then! I'm a bit scared that using DifferentialEquations.jl and packages based on it will be a little bit more complicated to use due to the extra part...
Like @yakir12, I friendly disagree here with the idea to remove this in favor of DimensionalData.jl As far as i know, DimensionalData.jl only allows named dimensions (I could be wrong)....
Yes it does, somehow completely missed the existence of that package. Thank you very much!
I'm getting similar problems on the latest version, is there any update on this? MWE: ``` julia using Optimization, OptimizationOptimJL, ForwardDiff, ComponentArrays rosenbrock(x, p) = (p.p1 - x.x1)^2 + p.p2...
I have some starting code here: I implemented this as a wrapper around solve. The pitfalls are that the `OptimizationFunction` passed to `remake` lacks the other fields of `OptimizationFunction` If...
> I couldn't understand your optimisation problem. Did you intend to write. My apologies for the late response, I somehow missed this in my mails. Maybe the following code is...
Apologies for the late response. The problem is what the value of the parameter `scale` should be. Taking a high value will let you have good fits, but too high...
Something like this works though ``` julia @assert false "This is an $(Crayon(foreground=:red)("error"))" ```
I suppose that the sample inside the vmap function fixes this