AutoOptimize.jl
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SciML
Automatic optimization and parallelization for Scientific Machine Learning (SciML)
This pull request changes the compat entry for the `ModelingToolkit` package from `3.11, 4.0, 5.0` to `3.11, 4.0, 5.0, 7`. This keeps the compat entries for earlier versions. Note: I...
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This pull request changes the compat entry for the `ModelingToolkit` package from `3.11, 4.0, 5.0` to `3.11, 4.0, 5.0, 6`. This keeps the compat entries for earlier versions. Note: I...
This pull request changes the compat entry for the `ModelingToolkit` package from `3.11, 4.0, 5.0` to `3.11, 4.0, 5.0, 6` . This keeps the compat entries for earlier versions. Note:...
This pull request changes the compat entry for the `ModelingToolkit` package from `3.11, 4.0, 5.0` to `3.11, 4.0, 5.0, 6`. This keeps the compat entries for earlier versions. Note: I...
This is probably a dumb question, but is something like this `auto_optimize`-able? ```julia function odefunc(du,u,p,t) du[:] .= Eh(t).*u end prob = ODEProblem(odefunc,[0.1,0.2],(0.0,10.0)) ``` I only know the function `Eh(t)` numerically...
We should run the analyses in tasks so that when the tasks hit a certain time limit they reject the analysis assuming it will fail.
What would be the best strategy for finding the right estimate of time to Hyper AutoOptimize? Should we perform a single step and benchmark the time taken and use it...
