SciML/NeuralPDE.jl

PINNs Lorenz param estim : kink in the output path of the predicted variables

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#284 创建于 2021年4月5日

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描述

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:

image

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