37 lines
1.1 KiB
Julia
37 lines
1.1 KiB
Julia
include("../../ho_basis.jl")
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include("../../EC.jl")
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V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
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Λ = 0
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m = 1.0
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ϕ = 0.1
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μω_global = 0.5
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E_max = 40
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H0 = get_3b_H_matrix(jacobi, V_of_r, μω_global, E_max, Λ, m, true, true)
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# Vp = perturbation to make the state artificially bound
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Vp_of_r(r) = -exp(-(r/3)^2)
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@time "Vp" Vp = get_3b_H_matrix(jacobi, Vp_of_r, μω_global, E_max, Λ, m, false, true)
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training_ref = 2 + 0.5im
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exact_E = [4.076642792419057-0.012998408352259658im,
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3.6129849325287-0.007397677539402868im,
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3.145212908643357-0.0038660337822150753im,
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2.6729225739451596-0.0021090370393881063im,
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2.196385760253282-0.0010430088245526555im,
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1.7162659936896967-0.0004515351140200029,
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1.2329926791785895-0.00017698044022813525im]
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training_c = (0.0 : 0.2 : 1.2) .* exp(-0.01im) #[-1.5 - 0.5im] .+ (randn(8) .+ 0.05im * randn(8))
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extrapolating_c = 0.0 : 0.2 : 1.2
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EC = affine_EC(H0, Vp)
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train!(EC, training_c; ref_eval=training_ref, CAEC=false)
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extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
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exportCSV(EC, "temp/3b_HO_XZ.csv")
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plot(EC, "temp/3b_HO_XZ.pdf")
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