julia> estimate = solve(estimation_prob, Tsit5(), saveat = solution.t)
┌ Warning: dt(8.881784197001252e-16) <= dtmin(8.881784197001252e-16) at t=2.0966388837394545, and step error estimate = 1.5129604958400626. Aborting. There is either an error in your model specification or the true solution is unstable.
└ @ SciMLBase ~/.julia/packages/SciMLBase/szsYq/src/integrator_interface.jl:599
retcode: DtLessThanMin
Interpolation: 1st order linear
t: 9-element Vector{Float64}:
0.0
0.25
0.5
0.75
1.0
1.25
1.5
1.75
2.0
u: 9-element Vector{Vector{Float64}}:
[3.1461493970111687, 1.5370475785612603]
[3.6818439489850294, 1.2765416819739916]
[4.5052732798471755, 1.1124706463392569]
[5.778503254169173, 1.0199394404085198]
[7.825801842962541, 0.9880422730295028]
[11.38347487175259, 1.0240247801514342]
[18.594915309958083, 1.1811076688536928]
[39.26724555866004, 1.7611949065256864]
[330.97703916071447, 14.982623648124799]
The tspan is (0.0, 5.0) but it is stopping at t = 2.0 and retcode is DtLessThanMin. The training of the neural network is good but the symbolic regression part is not working properly.
@ChrisRackauckas, the missing physics tutorial is erroring out in this line:
The tspan is (0.0, 5.0) but it is stopping at t = 2.0 and retcode is
DtLessThanMin
. The training of the neural network is good but the symbolic regression part is not working properly.gives
which looks very high.