40 lines
1.2 KiB
Julia
40 lines
1.2 KiB
Julia
include("../p_space.jl")
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include("../EC.jl")
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Λ = 0
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m = 1.0
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V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
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ϕ = 0.1
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vertices = [0, 2 * exp(-2im * ϕ), 3, 4]
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subdivisions = [15, 10, 10]
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jmax = 4
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E_max = 40
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μω_global = 0.5
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H0, weights = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m)
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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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Vp, _ = get_3b_H_matrix(jacobi, Vp_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, false, true)
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training_c = [2.6, 2.4, 2.2, 2.0, 1.8]
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extrapolating_c = 0.0 : 0.2 : 1.2
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training_ref = -2.22 # complete list not needed because identification is simple
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extrapolating_ref = [4.076662025307587-0.012709842443350328im,
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3.613318119833891-0.007335804709990623im,
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3.1453431847006783-0.004030580410326795im,
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2.672967129943755-0.00211498327461944im,
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2.196542557810288-0.0010719835443437104im,
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1.7164583929199813-0.0005455212208182736im,
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1.233088227541505-0.0003070320106485624im]
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EC = affine_EC(H0, Vp, weights)
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train!(EC, training_c; ref_eval=training_ref, CAEC=true)
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extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
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exportCSV(EC, "temp/Berggren_B2R.csv")
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plot(EC, "temp/Berggren_B2R.pdf") |