Herman Sletmoen
Herman Sletmoen
Thank you, I see and think that is an excellent direction to go. By scalarizing manually, do you mean to only define scalar variables and write out all the equations...
I think that would work if my equations were of that form, with `u` being fully unknown. But I have equations for `x` where `x[1,1]` is unknown and `x[1,2]` is...
Is a fix to this already underway with the PRs linked in #2747? If so I apologize for the spam 😅 Anyway, I think the example above motivates this feature...
I can kind of understand why. If I say that `x ~ 1.0` during initialization, it would be inconsistent to override that with `x => 2.0` in `remake`. But then...
**Bug 2:** This (initialization) problem warns that it is overdetermined, but should not be: ```julia @parameters c[1:1] d[1:1] @variables f(t) pdeps = [d[1] ~ c[1]] @named M = ODESystem([f ~...
> makes it easier to reason about the "parameter type" of a distribution and to avoid accidental type instabilities Thanks, I see what you mean. In this particular example I...
Not entirely sure if/how to proceed with this. * [Catalyst](https://github.com/SciML/ModelingToolkit.jl/actions/runs/9963200866/job/27528700518?pr=2865#step:6:1453) uses an equation like `parameter ~ independent variable` for a continuous event trigger and calls `check_equations()` manually, * [ModelingToolkitStandardLibrary ](https://github.com/SciML/ModelingToolkit.jl/actions/runs/9963200866/job/27528702029?pr=2865)...
That also struck my mind, so maybe this check is not a great idea to add now. One could also argue that it should be legal to have parameter-only equations...
@ChrisRackauckas > One of the changes down the line which may happen is that unknowns and parameters may be more fungible by structural simplification. I think this would be fantastic...