JuliusMartensen
JuliusMartensen
Not everything, but the most important parts are working again. I took the liberty to tag the current status as a release, since the optimization hyperparameters / sparse optimizers changed.
Hi! This is strange, I cannot reproduce this. Could you provide a more extensive minimal example? If I just do ```julia using DataDrivenDiffEq using LinearAlgebra X = randn(2,100) t =...
No worries. I just pasted it into the REPL and was able to reproduce the error. This is due to the type of `tsample` being `Float64` while the data in...
Hey there! Generally speaking, the methodology is universal :) . However, given the structure of the equations to recover and the magnitude and curvature of the training trajectory the result...
It seems you are using a newer version of DataDrivenDiffEq. The original script has been written with `v0.1.4` in mind, but the `SparseIdentificationResult` has been introduced later. Did you activate...
I'll check it today. The depreciation should only slow it down, not throw the error.
Resolved via #371
Validity checking of random symbolic expressions ( and for generating those ). ;)
With #371 you can use the optimizer directly on the data ( see the tests in /lib/DataDrivenSR/test ). ```Julia X, DX = data res = opt(X, DX, [options = DataDrivenCommonOptions()])...
Ah, sorry! **DataDrivenSparse** 😅 Have to find my way through the sublibs