Lyapunov spectrum of Lorenz system

Here, we present the calculation of the Lyapunov spectrum of the Lorenz system, using TaylorIntegration.jl. The computation involves evaluating the 1st order variational equations $\dot \xi = J \cdot \xi$ for this system, where $J = \operatorname{D}f$ is the Jacobian. By default, the numerical value of the Jacobian is computed using automatic differentiation techniques implemented in TaylorSeries.jl, which saves us from writing down explicitly the Jacobian. Conversely, this can be used to check a function implementing the Jacobian. As an alternative, specially important if performance is critical, the user may provide a Jacobian function.

The Lorenz system is the ODE defined as:

\[\begin{aligned} \dot{x}_1 & = \sigma(x_2-x_1), \\ \dot{x}_2 & = x_1(\rho-x_3)-x_2, \\ \dot{x}_3 & = x_1x_2-\beta x_3, \end{aligned}\]

where $\sigma$, $\rho$ and $\beta$ are constant parameters.

First, we write a Julia function which evaluates (in-place) the Lorenz system:

#Lorenz system ODE:
function lorenz!(dq, q, params, t)
    σ, ρ, β = params
    x, y, z = q
    dq[1] = σ*(y-x)
    dq[2] = x*(ρ-z)-y
    dq[3] = x*y-β*z
    nothing
end

Below, we use the the parameters $\sigma = 16.0$, $\beta = 4$ and $\rho = 45.92$.

#Lorenz system parameters
#we use the `const` prefix in order to help the compiler speed things up
const params = [16.0, 45.92, 4.0]

We define the initial conditions, the initial and final integration times for the integration:

const x0 = [19.0, 20.0, 50.0] #the initial condition
const t0 = 0.0     #the initial time
const tmax = 100.0 #final time of integration

Since the diagonal of the Jacobian is constant, the sum of the Lyapunov spectrum has to be equal to that value. We calculate this trace using TaylorSeries.jl, and after the numerical integration, we will come back to check if this value is conserved (or approximately conserved) as a function of time.

# Note that TaylorSeries.jl is @reexport-ed by TaylorIntegration.jl
# Calculate trace of Lorenz system Jacobian via TaylorSeries.jacobian:
import LinearAlgebra: tr
using TaylorIntegration
xi = set_variables("δ", order=1, numvars=length(x0))
x0TN = x0 .+ xi
dx0TN = similar(x0TN)
lorenz!(dx0TN, x0TN, params, t0)
jjac = TaylorSeries.jacobian(dx0TN)
lorenztr = tr(jjac) #trace of Lorenz system Jacobian matrix

As explained above, the user may provide a function which computes the Jacobian of the ODE in-place:

#Lorenz system Jacobian (in-place):
function lorenz_jac!(jac, x, params, t)
    σ, ρ, β = params
    jac[1,1] = -σ + zero(x[1])
    jac[1,2] = σ + zero(x[1])
    jac[1,3] = zero(x[1])
    jac[2,1] = ρ - x[3]
    jac[2,2] = -1.0 + zero(x[1])
    jac[2,3] = -x[1]
    jac[3,1] = x[2]
    jac[3,2] = x[1]
    jac[3,3] = -β + zero(x[1])
    nothing
end

We can actually check the consistency of lorenz_jac! with the computation of the jacobian using automatic differentiation techniques. Below we use the initial conditions x0, but it is easy to generalize this.

lorenz_jac!(jjac, x0, params, t0)  # update the matrix `jjac` using Jacobian provided by the user
TaylorSeries.jacobian(dx0TN) == jjac    # `dx0TN` is obtained via automatic differentiation

true

Now, we are ready to perform the integration using lyap_taylorinteg function, which integrates the 1st variational equations and uses Oseledets' theorem. The expansion order will be $28$ and the local absolute tolerance will be $10^{-20}$. lyap_taylorinteg will return three arrays: one with the evaluation times, one with the values of the dependent variables (at the time of evaluation), and another one with the values of the Lyapunov spectrum.

We first carry out the integration computing internally the Jacobian

tv, xv, λv = lyap_taylorinteg(lorenz!, x0, t0, tmax, 28, 1e-20, params; maxsteps=2000000);

Now, the integration is obtained exploiting lorenz_jac!:

tv_, xv_, λv_ = lyap_taylorinteg(lorenz!, x0, t0, tmax, 28, 1e-20, params, lorenz_jac!; maxsteps=2000000);

In terms of performance the second method is about ~50% faster than the first.

We check the consistency of the orbits computed by the two methods:

tv == tv_, xv == xv_, λv == λv_

(true, true, true)

As mentioned above, a more subtle check is related to the fact that the trace of the Jacobian is constant in time, which must coincide with the sum of all Lyapunov exponents. Using its initial value lorenztr, we compare it with the final Lyapunov exponents of the computation, and obtain

sum(λv[end,:]) ≈ lorenztr, sum(λv_[end,:]) ≈ lorenztr, sum(λv[end,:]) ≈ sum(λv_[end,:])

(true, true, true)

Above we checked the approximate equality; we now show that the relative error is quite small and comparable with the local machine epsilon value around lorenztr:

abs(sum(λv[end,:])/lorenztr - 1), abs(sum(λv_[end,:])/lorenztr - 1), eps(lorenztr)

(1.1102230246251565e-15, 1.1102230246251565e-15, 3.552713678800501e-15)

Therefore, the numerical error is dominated by roundoff errors in the floating point arithmetic of the integration. We will now proceed to plot our results. First, we plot Lorenz attractor in phase space

using Plots
plot(xv[:,1], xv[:,2], xv[:,3], leg=false)

We display now the Lyapunov exponents as a function of time:

using Plots
plot(tv, λv[:,1], label="L_1", legend=:right)
plot!(tv, λv[:,2], label="L_2")
plot!(tv, λv[:,3], label="L_3")
xlabel!("time")
ylabel!("L_i, i=1,2,3")
title!("Lyapunov exponents vs time")

This plot shows that the calculation of the Lyapunov exponents has converged.