62 lines
1.8 KiB
Julia
62 lines
1.8 KiB
Julia
using Plots
|
|
|
|
training_ref = -0.72763
|
|
exact_ref = 4.0766890719636635 - 0.01275892774109674im
|
|
|
|
training_c = [2.0, 1.9, 1.8]
|
|
extrapolating_c = 0.0 : 0.2 : 1.2
|
|
|
|
include("../ho_basis_3body_resonance.jl")
|
|
H0 = H
|
|
|
|
# Vp = perturbation to make the state artificially bound
|
|
Vp_of_r(r) = -exp(-(r/3)^2)
|
|
@time "Vp" Vp = get_src_V_matrix(Vp_of_r, basis, μω, μω_global)
|
|
|
|
exact = ComplexF64[]
|
|
training = ComplexF64[]
|
|
extrapolated = ComplexF64[]
|
|
training_vecs = Vector{ComplexF64}[]
|
|
|
|
current_E = training_ref
|
|
|
|
for c in training_c
|
|
println("Training for c = $c")
|
|
local H = H0 + c .* Vp
|
|
local evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
|
|
|
|
global current_E = nearest(evals, current_E)
|
|
push!(training, current_E)
|
|
push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
|
|
end
|
|
|
|
# CA-EC
|
|
training_vecs = vcat(training_vecs, conj(training_vecs))
|
|
|
|
EC_basis = hcat(training_vecs...)
|
|
N_EC = transpose(EC_basis) * EC_basis
|
|
H0_EC = transpose(EC_basis) * H0 * EC_basis
|
|
Vp_EC = transpose(EC_basis) * Vp * EC_basis
|
|
|
|
current_E = exact_ref
|
|
|
|
for c in extrapolating_c
|
|
println("Extrapolating for c = $c")
|
|
local H = H0 + c .* Vp
|
|
local evals, _ = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
|
|
|
|
global current_E = nearest(evals, current_E)
|
|
push!(exact, current_E)
|
|
|
|
# extrapolation
|
|
H_EC = H0_EC + c .* Vp_EC
|
|
evals = eigvals(H_EC, N_EC)
|
|
push!(extrapolated, nearest(evals, current_E))
|
|
end
|
|
|
|
exportCSV("temp/HO_B2R.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))
|
|
|
|
scatter(real.(training),imag.(training), label="training")
|
|
scatter!(real.(exact),imag.(exact), label="exact")
|
|
scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
|
|
savefig("temp/HO_B2R.pdf") |