🟡 Extended Lorenz-84 model (codim 2 + BT/ZH aBS)

• 🟡 Extended Lorenz-84 model (codim 2 + BT/ZH aBS)
• • Problem setting
  • Continuation and codim 1 bifurcations
  • Continuation of Fold points
  • Continuation of Hopf points
  • Continuation of Hopf points from the Bogdanov-Takens point
  • Continuation of Hopf points from the Zero-Hopf point
  • Plotting with Makie.jl
  • References

In this tutorial, we study the extended Lorenz-84 model which is also treated in MatCont ^[Kuznetsov]. This model is interesting because it features all codim 2 bifurcations of equilibria. It is thus convenient to test our algorithms.

After this tutorial, you will be able to

• detect codim 1 bifurcation Fold / Hopf / Branch point
• follow Fold / Hopf points and detect codim 2 bifurcation points
• branch from the codim 2 points to curves of Fold / Hopf points

The model is as follows

\[\left\{\begin{array}{l} \dot{X}=-Y^{2}-Z^{2}-\alpha X+\alpha F-\gamma U^{2} \\ \dot{Y}=X Y-\beta X Z-Y+G \\ \dot{Z}=\beta X Y+X Z-Z \\ \dot{U}=-\delta U+\gamma U X+T \end{array}\right.\tag{E}\]

We start with some imports:

using Revise, Plotsimport BifurcationKit as BKimport BifurcationKit: @optic

Problem setting

We can now encode the vector field (E) in a function.

# vector fieldfunction Lor(u, p)	(;α,β,γ,δ,G,F,T) = p	X,Y,Z,U = u	[		-Y^2 - Z^2 - α*X + α*F - γ*U^2,		X*Y - β*X*Z - Y + G,		β*X*Y + X*Z - Z,		-δ*U + γ*U*X + T	]end# parameter valuesparlor = (α = 1//4, β = 1, G = .25, δ = 1.04, γ = 0.987, F = 1.7620532879639, T = .0001265)# initial conditionz0 = [2.9787004394953343, -0.03868302503393752,  0.058232737694740085, -0.02105288273117459]# bifurcation problemrecord_from_solution_Lor(x, p; k...) = (X = x[1], Y = x[2], Z = x[3], U = x[4])prob = BK.ODEBifProblem(Lor, z0, (parlor..., T=0.04, F=3.), (@optic _.F);    record_from_solution = record_from_solution_Lor)

Continuation and codim 1 bifurcations

Once the problem is set up, we can continue the state w.r.t. $F$ and detect codim 1 bifurcations. This is achieved as follows:

# continuation optionsopts_br = BK.ContinuationPar(p_min = -1.5, p_max = 3.0, ds = 0.002, dsmax = 0.15,	# Optional: bisection options for locating bifurcations	n_inversion = 6,	# number of eigenvalues	nev = 4)# compute the branch of solutionsbr = BK.continuation(prob, BK.PALC(), opts_br;	normC = BK.norminf,	bothside = true)scene = plot(br, plotfold = false, markersize = 4, legend = :topleft)

With detailed information:

br

 ┌─ Curve type: EquilibriumCont
 ├─ Number of points: 33
 ├─ Type of vectors: Vector{Float64}
 ├─ Parameter F starts at 3.0, ends at 3.0
 ├─ Algo: PALC [Secant]
 └─ Special points:

- #  1, endpoint at F ≈ +3.00000000,                                                                     step =   0
- #  2,     hopf at F ≈ +2.85996783 ∈ (+2.85986480, +2.85996783), |δp|=1e-04, [converged], δ = ( 2,  2), step =   1
- #  3,     hopf at F ≈ +2.46723305 ∈ (+2.46720734, +2.46723305), |δp|=3e-05, [converged], δ = (-2, -2), step =   3
- #  4,     hopf at F ≈ +1.61975642 ∈ (+1.61959602, +1.61975642), |δp|=2e-04, [converged], δ = ( 2,  2), step =   9
- #  5,       bp at F ≈ +1.54664839 ∈ (+1.54664837, +1.54664839), |δp|=1e-08, [converged], δ = (-1,  0), step =  11
- #  6, endpoint at F ≈ +3.00000000,                                                                     step =  32

Continuation of Fold points

We follow the Fold points in the parameter plane $(T,F)$. We tell the solver to consider br.specialpoint[5] and continue it.

# function to record the current statesn_codim2 = BK.continuation(br, 5, (@optic _.T),	BK.ContinuationPar(opts_br, p_max = 3.2, p_min = -0.1,		dsmin=1e-5, ds = -0.001, dsmax = 0.005) ;	normC = BK.norminf,	# we save the different components for plotting	record_from_solution = record_from_solution_Lor,	)scene = plot(sn_codim2, vars=(:X, :U), branchlabel = "Folds", ylims=(-0.5, 0.5))

with detailed information

sn_codim2

 ┌─ Curve type: FoldCont
 ├─ Number of points: 82
 ├─ Type of vectors: Vector{Float64}
 ├─ Parameters (:F, :T)
 ├─ Parameter T starts at 0.04, ends at -0.1
 ├─ Algo: PALC [Secant]
 └─ Special points:

- #  1,       bt at T ≈ +0.02094014 ∈ (+0.02094014, +0.02094020), |δp|=6e-08, [converged], δ = ( 0,  0), step =  12
- #  2,       zh at T ≈ +0.00012644 ∈ (+0.00012644, +0.00012666), |δp|=2e-07, [converged], δ = ( 0,  0), step =  29
- #  3,       zh at T ≈ -0.00012655 ∈ (-0.00012655, -0.00012644), |δp|=1e-07, [converged], δ = ( 0,  0), step =  32
- #  4,       bt at T ≈ -0.02094041 ∈ (-0.02094041, -0.02093949), |δp|=9e-07, [converged], δ = ( 0,  0), step =  49
- #  5, endpoint at T ≈ -0.10000000,                                                                     step =  81

For example, we can compute the following normal form

BK.get_normal_form(sn_codim2, 1)

Bogdanov-Takens bifurcation point at (:F, :T) ≈ (1.4467165285461687, 0.02094013962410275).
Normal form (B, β1 + β2⋅B + b⋅A⋅B + a⋅A²)
Normal form coefficients:
 a = 0.21442327431975317
 b = 0.6065142321262077

You can call various predictors:
 - predictor(::BogdanovTakens, ::Val{:HopfCurve}, ds)
 - predictor(::BogdanovTakens, ::Val{:FoldCurve}, ds)
 - predictor(::BogdanovTakens, ::Val{:HomoclinicCurve}, ds)

Continuation of Hopf points

We follow the Hopf points in the parameter plane $(T,F)$. We tell the solver to consider br.specialpoint[3] and continue it.

hp_codim2_1 = BK.continuation(br, 3, (@optic _.T),	BK.ContinuationPar(opts_br, ds = -0.001, dsmax = 0.02, dsmin = 1e-4) ;	normC = BK.norminf,	# we save the different components for plotting	record_from_solution = record_from_solution_Lor,	# compute both sides of the initial condition	bothside = true,	)plot(sn_codim2, vars=(:X, :U), branchlabel = "Folds")plot!(hp_codim2_1, vars=(:X, :U), branchlabel = "Hopfs")ylims!(-0.7, 0.7); xlims!(1, 1.3)

hp_codim2_1

 ┌─ Curve type: HopfCont
 ├─ Number of points: 429
 ├─ Type of vectors: Vector{Float64}
 ├─ Parameters (:F, :T)
 ├─ Parameter T starts at 0.020940169755303512, ends at -0.15356949274656534
 ├─ Algo: PALC [Secant]
 └─ Special points:

- #  1, endpoint at T ≈ +0.02094017,                                                                     step =   0
- #  2,       bt at T ≈ +0.02094017 ∈ (+0.02094017, +0.02094017), |δp|=6e-11, [converged], δ = ( 0,  0), step =   0
- #  3,       gh at T ≈ +0.05019751 ∈ (+0.05019655, +0.05019751), |δp|=1e-06, [converged], δ = ( 0,  0), step =  19
- #  4,       hh at T ≈ +0.02627340 ∈ (+0.02627340, +0.02627528), |δp|=2e-06, [converged], δ = (-2, -2), step =  35
- #  5, endpoint at T ≈ -0.15372759,                                                                     step = 429

For example, we can compute the following normal form

BK.get_normal_form(hp_codim2_1, 3)

Bautin bifurcation point at (:F, :T) ≈ (2.3763590366726257, 0.05019751302158555).
ω = 0.6903670769045954
Second lyapunov coefficient l₂ = 0.1557753180138303
Normal form: i⋅ω⋅z + l₂⋅z⋅|z|⁴

Continuation of Hopf points from the Bogdanov-Takens point

When we computed the curve of Fold points, we detected a Bogdanov-Takens bifurcation. We can branch from it to get the curve of Hopf points. This is done as follows:

hp_from_bt = BK.continuation(sn_codim2, 4,	BK.ContinuationPar(opts_br, ds = -0.001, dsmax = 0.02, dsmin = 1e-4) ;	normC = BK.norminf,	# detection of codim 2 bifurcations with bisection	detect_codim2_bifurcation = 2,	# we save the different components for plotting	record_from_solution = record_from_solution_Lor,	)plot(sn_codim2, vars=(:X, :U), branchlabel = "SN")plot!(hp_codim2_1, vars=(:X, :U), branchlabel = "Hopf1")plot!(hp_from_bt,  vars=(:X, :U), branchlabel = "Hopf2")ylims!(-0.7, 0.75); xlims!(0.95, 1.3)

with detailed information

hp_from_bt

 ┌─ Curve type: HopfCont from BogdanovTakens bifurcation point.
 ├─ Number of points: 401
 ├─ Type of vectors: Vector{Float64}
 ├─ Parameters (:F, :T)
 ├─ Parameter T starts at -0.026824826790442673, ends at 0.15063717400281545
 ├─ Algo: PALC [Secant]
 └─ Special points:

- #  1,       gh at T ≈ -0.05018361 ∈ (-0.05022014, -0.05018361), |δp|=4e-05, [converged], δ = ( 0,  0), step =  23
- #  2,       hh at T ≈ -0.02626030 ∈ (-0.02631231, -0.02626030), |δp|=5e-05, [converged], δ = (-2, -2), step =  26
- #  3, endpoint at T ≈ +0.15079813,                                                                     step = 401

Continuation of Hopf points from the Zero-Hopf point

When we computed the curve of Fold points, we detected a Zero-Hopf bifurcation. We can branch from it to get the curve of Hopf points. This is done as follows:

hp_from_zh = BK.continuation(sn_codim2, 2,	BK.ContinuationPar(opts_br, ds = 0.001, dsmax = 0.02) ;	normC = BK.norminf,	detect_codim2_bifurcation = 2,	record_from_solution = record_from_solution_Lor,	)plot(hp_codim2_1, vars=(:X, :U), branchlabel = "Hopf")plot!(hp_from_bt, vars=(:X, :U), branchlabel = "Hopf2")plot!(hp_from_zh, vars=(:X, :U), branchlabel = "Hopf", legend = :topleft)plot!(sn_codim2,vars=(:X, :U),)ylims!(-0.7,0.75); xlims!(0.95,1.3)

with detailed information

hp_from_zh

 ┌─ Curve type: HopfCont from ZeroHopf bifurcation point.
 ├─ Number of points: 401
 ├─ Type of vectors: Vector{Float64}
 ├─ Parameters (:F, :T)
 ├─ Parameter T starts at 0.00012643994894431058, ends at 0.6669634456430228
 ├─ Algo: PALC [Secant]
 └─ Special points:

- #  1,       gh at T ≈ +0.00012660 ∈ (+0.00012654, +0.00012660), |δp|=6e-08, [converged], δ = ( 0,  0), step =   1
- #  2,       hh at T ≈ +0.02627444 ∈ (+0.02627324, +0.02627444), |δp|=1e-06, [converged], δ = ( 2,  2), step =  27
- #  3, endpoint at T ≈ +0.66778051,                                                                     step = 401

Plotting with Makie.jl

using CairoMakief,ax = BK.plot(hp_codim2_1, vars=(:X, :U), branchlabel = "Hopf", dash_unstable_style = true)BK.plot!(ax, hp_from_bt, vars=(:X, :U), branchlabel = "Hopf2", dash_unstable_style = true)BK.plot!(ax, hp_from_zh, vars=(:X, :U), branchlabel = "Hopf", dash_unstable_style = true)BK.plot!(ax, sn_codim2,vars=(:X, :U), dash_unstable_style = true)Makie.ylims!(ax, -0.7,0.75); Makie.xlims!(ax, 0.95,1.3)f

References