SciML/NeuralPDE.jl
PINNs Lorenz param estim : kink in the output path of the predicted variables
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#284 geöffnet am 05.04.2021
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Beschreibung
Running the Lorenz param estim example, the estimated parameters are:
3-element Vector{Float64}: 10.000484105285961 27.99897160477475 2.667291549846074
Then in the graphical analysis:
initθ = discretization.init_params acum = [0;accumulate(+, length.(initθ))] sep = [acum[i]+1 : acum[i+1] for i in 1:length(acum)-1] minimizers = [res.minimizer[s] for s in sep] ts = [domain.domain.lower:dt/10:domain.domain.upper for domain in domains][1] u_predict = [[discretization.phii[1] for t in ts] for i in 1:3] plot(sol) plot!(ts, u_predict, label = ["x(t)" "y(t)" "z(t)"])
produces a kink in the x, predicted x variables different from the published graph as follows:
