64 lines
1.9 KiB
Julia
64 lines
1.9 KiB
Julia
using Plots
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training_ref = -0.72763
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exact_ref = 4.0766890719636635 - 0.01275892774109674im
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training_c = [2.0, 1.9, 1.8]
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extrapolating_c = 0.0 : 0.2 : 1.2
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include("../ho_basis_3body_resonance.jl")
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H0 = H
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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_src_V_matrix(Vp_of_r, basis, μω, μω_global)
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exact = ComplexF64[]
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training = ComplexF64[]
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extrapolated = ComplexF64[]
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training_vecs = Vector{ComplexF64}[]
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current_E = training_ref
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for c in training_c
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println("Training for c = $c")
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local H = H0 + c .* Vp
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local evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
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global current_E = nearest(evals, current_E)
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push!(training, current_E)
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push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
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end
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# CA-EC
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training_vecs = vcat(training_vecs, conj(training_vecs))
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println("Original EC dimensionality = $(length(training_vecs))")
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@time "Gram-Schmidt" training_vecs = gram_schmidt!(training_vecs; verbose=true) # orthonormalization
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EC_basis = hcat(training_vecs...)
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H0_EC = transpose(EC_basis) * H0 * EC_basis
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Vp_EC = transpose(EC_basis) * Vp * EC_basis
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current_E = exact_ref
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for c in extrapolating_c
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println("Extrapolating for c = $c")
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local H = H0 + c .* Vp
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local evals, _ = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
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global current_E = nearest(evals, current_E)
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push!(exact, current_E)
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# extrapolation
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H_EC = H0_EC + c .* Vp_EC
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evals = eigvals(H_EC)
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push!(extrapolated, nearest(evals, current_E))
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end
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exportCSV("temp/HO_B2R.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))
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scatter(real.(training),imag.(training), label="training")
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scatter!(real.(exact),imag.(exact), label="exact")
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scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
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savefig("temp/HO_B2R.pdf") |