Demo - Cubic nonlinear Klein-Gordon equation


Mikael Mortensen (mikaem at


Nov 14, 2019

Summary. This is a demonstration of how the Python module shenfun can be used to solve the time-dependent, nonlinear Klein-Gordon equation, in a triply periodic domain. The demo is implemented in a single Python file, and it may be run in parallel using MPI. The Klein-Gordon equation is solved using a mixed formulation. The discretization, and some background on the spectral Galerkin method is given first, before we turn to the actual details of the shenfun implementation.

The nonlinear Klein-Gordon equation

Movie showing the evolution of the solution \(u\) from Eq. (1), in a slice through the center of the domain, computed with the code described in this demo.

Model equation

The cubic nonlinear Klein-Gordon equation is a wave equation important for many scientific applications such as solid state physics, nonlinear optics and quantum field theory [Waz08]. The equation is given as

(1)\[ \frac{\partial^2 u}{\partial t^2} = \nabla^2 u - \gamma(u - u|u|^2) \quad \text{for} \, u \in \Omega,\]

with initial conditions

(2)\[ u(\boldsymbol{x}, t=0) = u^0 \quad \text{and} \quad \frac{\partial u(\boldsymbol{x}, t=0)}{\partial t} = u_t^0.\]

The spatial coordinates are here denoted as \(\boldsymbol{x} = (x, y, z)\), and \(t\) is time. The parameter \(\gamma=\pm 1\) determines whether the equations are focusing (\(+1\)) or defocusing (\(-1\)) (in the movie we have used \(\gamma=1\)). The domain \(\Omega=[-2\pi, 2\pi]^3\) is triply periodic and initial conditions will here be set as

(3)\[ u^0 = 0.1 \exp \left( -\boldsymbol{x} \cdot \boldsymbol{x} \right),\]
(4)\[ u_t^0 = 0.\]

We will solve these equations using a mixed formulation and a spectral Galerkin method. The mixed formulation reads

(5)\[ \frac{\partial f}{\partial t} = \nabla^2 u - \gamma (u - u|u|^2),\]
(6)\[ \frac{\partial u}{\partial t} = f.\]

The energy of the solution can be computed as

(7)\[ E(u) = \int_{\Omega} \left( \frac{1}{2} f^2 + \frac{1}{2}|\nabla u|^2 + \gamma(\frac{1}{2}u^2 - \frac{1}{4}u^4) \right) dx\]

and it is crucial that this energy remains constant in time.

The movie (The nonlinear Klein-Gordon equation) is showing the solution \(u\), computed with the code shown in the bottom of Sec. Complete solver.

Spectral Galerkin formulation

The PDEs in (5) and (6) can be solved with many different numerical methods. We will here use the shenfun software and this software makes use of the spectral Galerkin method. Being a Galerkin method, we need to reshape the governing equations into proper variational forms, and this is done by multiplying (5) and (6) with the complex conjugate of proper test functions and then integrating over the domain. To this end we use testfunctions \(g\in V(\Omega)\) with Eq. (5) and \(v \in V(\Omega)\) with Eq. (6), where \(V(\omega)\) is a suitable function space, and obtain

(8)\[ \frac{\partial}{\partial t} \int_{\Omega} f\, \overline{g}\, w \,dx = \int_{\Omega} \left(\nabla^2 u - \gamma( u\, - u|u|^2) \right) \overline{g} \, w \,dx,\]
(9)\[ \frac{\partial }{\partial t} \int_{\Omega} u\, \overline{v}\, w \, dx = \int_{\Omega} f\, \overline{v} \, w \, dx.\]

Note that the overline is used to indicate a complex conjugate, and \(w\) is a weight function associated with the test functions. The functions \(f\) and \(u\) are now to be considered as trial functions, and the integrals over the domain are often referred to as inner products. With inner product notation

\[\left(u, v\right) = \int_{\Omega} u \, \overline{v} \, w\, dx.\]

and an integration by parts on the Laplacian, the variational problem can be formulated as:

(10)\[ \frac{\partial}{\partial t} (f, g) = -(\nabla u, \nabla g) -\gamma \left( u - u|u|^2, g \right),\]
(11)\[ \frac{\partial }{\partial t} (u, v) = (f, v).\]

The time and space discretizations are still left open. There are numerous different approaches that one could take for discretizing in time, and the first two terms on the right hand side of (10) can easily be treated implicitly as well as explicitly. However, the approach we will follow in Sec. (Runge-Kutta integrator) is a fully explicit 4th order Runge-Kutta method.


To find a numerical solution we need to discretize the continuous problem (10) and (11) in space as well as time. Since the problem is triply periodic, Fourier exponentials are normally the best choice for trial and test functions, and as such we use basis functions

(12)\[ \phi_l(x) = e^{\imath \underline{l} x}, \quad -\infty < l < \infty,\]

where \(l\) is the wavenumber, and \(\underline{l}=\frac{2\pi}{L}l\) is the scaled wavenumber, scaled with domain length \(L\) (here \(4\pi\)). Since we want to solve these equations on a computer, we need to choose a finite number of test functions. A function space \(V^N\) can be defined as

(13)\[ V^N(x) = \text{span} \{\phi_l(x)\}_{l\in \boldsymbol{l}},\]

where \(N\) is chosen as an even positive integer and \(\boldsymbol{l} = (-N/2, -N/2+1, \ldots, N/2-1)\). And now, since \(\Omega\) is a three-dimensional domain, we can create tensor products of such bases to get, e.g., for three dimensions

(14)\[ W^{\boldsymbol{N}}(x, y, z) = V^N(x) \otimes V^N(y) \otimes V^N(z),\]

where \(\boldsymbol{N} = (N, N, N)\). Obviously, it is not necessary to use the same number (\(N\)) of basis functions for each direction, but it is done here for simplicity. A 3D tensor product basis function is now defined as

(15)\[ \Phi_{lmn}(x,y,z) = e^{\imath \underline{l} x} e^{\imath \underline{m} y} e^{\imath \underline{n} z} = e^{\imath (\underline{l}x + \underline{m}y + \underline{n}z)}\]

where the indices for \(y\)- and \(z\)-direction are \(\underline{m}=\frac{2\pi}{L}m, \underline{n}=\frac{2\pi}{L}n\), and \(\boldsymbol{m}\) and \(\boldsymbol{n}\) are the same as \(\boldsymbol{l}\) due to using the same number of basis functions for each direction. One distinction, though, is that for the \(z\)-direction expansion coefficients are only stored for \(n=(0, 1, \ldots, N/2)\) due to Hermitian symmetry (real input data).

We now look for solutions of the form

(16)\[ u(x, y, z, t) = \sum_{n=-N/2}^{N/2-1}\sum_{m=-N/2}^{N/2-1}\sum_{l=-N/2}^{N/2-1} \hat{u}_{lmn} (t)\Phi_{lmn}(x,y,z).\]

The expansion coefficients \(\hat{\boldsymbol{u}} = \{\hat{u}_{lmn}(t)\}_{(l,m,n) \in \boldsymbol{l} \times \boldsymbol{m} \times \boldsymbol{n}}\) can be related directly to the solution \(u(x, y, z, t)\) using Fast Fourier Transforms (FFTs) if we are satisfied with obtaining the solution in quadrature points corresponding to

(17)\[ x_i = \frac{4 \pi i}{N}-2\pi \quad \forall \, i \in \boldsymbol{i}, \text{where}\, \boldsymbol{i}=(0,1,\ldots,N-1),\]
(18)\[ y_j = \frac{4 \pi j}{N}-2\pi \quad \forall \, j \in \boldsymbol{j}, \text{where}\, \boldsymbol{j}=(0,1,\ldots,N-1),\]
(19)\[ z_k = \frac{4 \pi k}{N}-2\pi \quad \forall \, k \in \boldsymbol{k}, \text{where}\, \boldsymbol{k}=(0,1,\ldots,N-1).\]

Note that these points are different from the standard (like \(2\pi j/N\)) since the domain is set to \([-2\pi, 2\pi]^3\) and not the more common \([0, 2\pi]^3\). We have

(20)\[ \boldsymbol{u} = \mathcal{F}_k^{-1}\left(\mathcal{F}_j^{-1}\left(\mathcal{F}_i^{-1}\left(\hat{\boldsymbol{u}}\right)\right)\right)\]

with \(\boldsymbol{u} = \{u(x_i, y_j, z_k)\}_{(i,j,k)\in \boldsymbol{i} \times \boldsymbol{j} \times \boldsymbol{k}}\) and where \(\mathcal{F}_i^{-1}\) is the inverse Fourier transform along the direction of index \(i\), for all \((j, k) \in \boldsymbol{j} \times \boldsymbol{k}\). Note that the three inverse FFTs are performed sequentially, one direction at the time, and that there is no scaling factor due to the definition used for the inverse Fourier transform

(21)\[ u(x_j) = \sum_{l=-N/2}^{N/2-1} \hat{u}_l e^{\imath \underline{l} x_j}, \quad \,\, \forall \, j \in \, \boldsymbol{j}.\]

Note that this differs from the definition used by, e.g., Numpy.

The inner products used in Eqs. (10), (11) may be computed using forward FFTs. However, there is a tiny detail that deserves a comment. The regular Fourier inner product is given as

\[\int_{0}^{L} e^{\imath \underline{k}x} e^{- \imath \underline{l}x} dx = L\, \delta_{kl}\]

where a weight function is chosen as \(w(x) = 1\) and \(\delta_{kl}\) equals unity for \(k=l\) and zero otherwise. In Shenfun we choose instead to use a weight function \(w(x)=1/L\), such that the weighted inner product integrates to unity:

\[\int_{0}^{L} e^{\imath \underline{k}x} e^{- \imath \underline{l}x} \frac{1}{L} dx = \delta_{kl}.\]

With this weight function the scalar product and the forward transform are the same and we obtain:

(22)\[ \left(u, \Phi_{lmn}\right) = \hat{u}_{lmn} = \left(\frac{1}{N}\right)^3 \mathcal{F}_l\left(\mathcal{F}_m\left(\mathcal{F}_n\left(\boldsymbol{u}\right)\right)\right) \quad \forall (l,m,n) \in \boldsymbol{l} \times \boldsymbol{m} \times \boldsymbol{n},\]

From this we see that the variational forms (10) and (11) may be written in terms of the Fourier transformed quantities \(\hat{\boldsymbol{u}}\) and \(\hat{\boldsymbol{f}}\). Expanding the exact derivatives of the nabla operator, we have

(23)\[ (\nabla u, \nabla v) = (\underline{l}^2+\underline{m}^2+\underline{n}^2)\hat{u}_{lmn},\]
(24)\[ (u, v) = \hat{u}_{lmn},\]
(25)\[ (u|u|^2, v) = \widehat{u|u|^2}_{lmn}\]

and as such the equations to be solved for each wavenumber can be found directly as

(26)\[ \frac{\partial \hat{f}_{lmn}}{\partial t} = \left(-(\underline{l}^2+\underline{m}^2+\underline{n}^2+\gamma)\hat{u}_{lnm} + \gamma \widehat{u|u|^2}_{lnm}\right),\]
(27)\[ \frac{\partial \hat{u}_{lnm}}{\partial t} = \hat{f}_{lnm}.\]

There is more than one way to arrive at these equations. Taking the 3D Fourier transform of both equations (5) and (6) is one obvious way. With the Python module shenfun, one can work with the inner products as seen in (10) and (11), or the Fourier transforms directly. See for example Sec. Runge-Kutta integrator for how \((\nabla u, \nabla v)\) can be implemented. In short, shenfun contains all the tools required to work with the spectral Galerkin method, and we will now see how shenfun can be used to solve the Klein-Gordon equation.

For completion, we note that the discretized problem to solve can be formulated with the Galerkin method as: for all \(t>0\), find \((f, u) \in W^N \times W^N\) such that

(28)\[ \frac{\partial}{\partial t} (f, g) = -(\nabla u, \nabla g) -\gamma \left( u - u|u|^2, g \right),\]
(29)\[ \frac{\partial }{\partial t} (u, v) = (f, v) \quad \forall \, (g, v) \in W^N \times W^N.\]

where \(u(x, y, z, 0)\) and \(f(x, y, z, 0)\) are given as the initial conditions according to Eq. (2).


To solve the Klein-Gordon equations we need to make use of the Fourier bases in shenfun, and these base are found in submodule shenfun.fourier.bases. The triply periodic domain allows for Fourier in all three directions, and we can as such create one instance of this base class using Basis() with family Fourier for each direction. However, since the initial data are real, we can take advantage of Hermitian symmetries and thus make use of a real to complex class for one (but only one) of the directions, by specifying dtype='d'. We can only make use of the real-to-complex class for the direction that we choose to transform first with the forward FFT, and the reason is obviously that the output from a forward transform of real data is now complex. We may start implementing the solver as follows

from shenfun import *
from mpi4py import MPI
import numpy as np

# Set size of discretization
N = (32, 32, 32)

# Create bases
K0 = Basis(N[0], 'F', domain=(-2*np.pi, 2*np.pi), dtype='D')
K1 = Basis(N[1], 'F', domain=(-2*np.pi, 2*np.pi), dtype='D')
K2 = Basis(N[2], 'F', domain=(-2*np.pi, 2*np.pi), dtype='d')

We now have three instances K0, K1 and K2, corresponding to the space (13), that each can be used to solve one-dimensional problems. However, we want to solve a 3D problem, and for this we need a tensor product space, like (14), created as a tensor product of these three spaces

# Create communicator
T = TensorProductSpace(comm, (K0, K1, K2), **{'planner_effort':

Here the planner_effort, which is a flag used by FFTW, is optional. Possibel choices are from the list (FFTW_ESTIMATE, FFTW_MEASURE, FFTW_PATIENT, FFTW_EXHAUSTIVE), and the flag determines how much effort FFTW puts in looking for an optimal algorithm for the current platform. Note that it is also possible to use FFTW wisdom with shenfun, and as such, for production, one may perform exhaustive planning once and then simply import the result of that planning later, as wisdom.

The TensorProductSpace instance T contains pretty much all we need for computing inner products or fast transforms between real and wavenumber space. However, since we are going to solve for a mixed system, it is convenient to also use the MixedTensorProductSpace class

TT = MixedTensorProductSpace([T, T])

We need containers for the solution as well as intermediate work arrays for, e.g., the Runge-Kutta method. Arrays are created as

uf = Array(TT)           # Solution array in physical space
u, f = uf                # Split solution array by creating two views u and f
duf = Function(TT)       # Array for right hand sides
du, df = duf             # Split into views
uf_hat = Function(TT)    # Solution in spectral space
uf_hat0 = Function(TT)   # Work array 1
uf_hat1 = Function(TT)   # Work array 2
u_hat, f_hat = uf_hat    # Split into views

The Array class is a subclass of Numpy’s ndarray, without much more functionality than constructors that return arrays of the correct shape according to the basis used in the construction. The Array represents the left hand side of (16), evaluated on the quadrature mesh. A different type of array is returned by the Function class, that subclasses both Nympy’s ndarray as well as an internal BasisFunction class. An instance of the Function represents the entire spectral Galerkin function (16). As such, it can be used in complex variational linear forms. For example, if you want to compute the partial derivative \(\partial u/\partial x\), then this may be achieved by projection, i.e., find \(u_x \in V^N\) such that \((u_x-\partial u/\partial x, v) = 0\), for all \(v \in V^N\). This projection may be easily computed in shenfun using

ux = project(Dx(u_hat, 0, 1), T)

The following code, on the other hand, will raise an error since you cannot take the derivative of an interpolated Array u, only a Function

    project(Dx(u, 0, 1), T)
except AssertionError:
    print("AssertionError: Dx not for Arrays")


The solution array uf and its transform uf_hat need to be initialized according to Eq. (2). To this end it is convenient (but not required, we could just as easily use Numpy for this as well) to use Sympy, which is a Python library for symbolic mathamatics.

from sympy import symbols, exp, lambdify

x, y, z = symbols("x,y,z")
ue = 0.1*exp(-(x**2 + y**2 + z**2))
ul = lambdify((x, y, z), ue, 'numpy')
X = T.local_mesh(True)
u[:] = Array(T, buffer=ul(*X))
u_hat = T.forward(u, u_hat)

Here X is a list of the three mesh coordinates (x, y, z) local to the current processor. Each processor has its own part of the computational mesh, and the distribution is handled during the creation of the TensorProductSpace class instance T. There is no need to do anything about the f/f_hat arrays since they are already initialized by default to zero. Note that calling the ul function with the argument *X is the same as calling with X[0], X[1], X[2].

Runge-Kutta integrator

A fourth order explicit Runge-Kutta integrator requires only a function that returns the right hand sides of (26) and (27). Such a function can be implemented as

# focusing (+1) or defocusing (-1)
gamma = 1
uh = TrialFunction(T)
vh = TestFunction(T)
k2 = -(inner(grad(vh), grad(uh)).scale  + gamma)

def compute_rhs(duf_hat, uf_hat, up, Tp, w0):
    u_hat, f_hat = uf_hat
    du_hat, df_hat = duf_hat
    df_hat[:] = k2*u_hat
    up = Tp.backward(u_hat, up)
    df_hat += Tp.forward(gamma*up**3, w0)
    du_hat[:] = f_hat
    return duf_hat

The code is fairly self-explanatory. k2 represents the coefficients in front of the linear \(\hat{u}\) in (26). The output array is duf_hat, and the input array is uf_hat, whereas up and w0 are work arrays. The array duf_hat contains the right hand sides of both (26) and (27), where the linear and nonlinear terms are recognized in the code as comments (1) and (2). The array uf_hat contains the solution at initial and intermediate Runge-Kutta steps.

With a function that returns the right hand side in place, the actual integrator can be implemented as

w0 = Function(T)
a = [1./6., 1./3., 1./3., 1./6.]         # Runge-Kutta parameter
b = [0.5, 0.5, 1.]                       # Runge-Kutta parameter
t = 0
dt = 0.01
end_time = 1.0
while t < end_time-1e-8:
    t += dt
    uf_hat1[:] = uf_hat0[:] = uf_hat
    for rk in range(4):
        duf = compute_rhs(duf, uf_hat, u, T, w0)
        if rk < 3:
            uf_hat[:] = uf_hat0 + b[rk]*dt*duf
        uf_hat1 += a[rk]*dt*duf
    uf_hat[:] = uf_hat1

Complete solver

A complete solver is given below, with intermediate plotting of the solution and intermediate computation of the total energy. Note that the total energy is unchanged to 8 decimal points at \(t=100\).

from sympy import symbols, exp, lambdify
import numpy as np
import matplotlib.pyplot as plt
from mpi4py import MPI
from time import time
from shenfun import *

rank = comm.Get_rank()

# Use sympy to set up initial condition
x, y, z = symbols("x,y,z")
ue = 0.1*exp(-(x**2 + y**2 + z**2))
ul = lambdify((x, y, z), ue, 'numpy')

# Size of discretization
N = (64, 64, 64)

# Defocusing or focusing
gamma = 1

K0 = Basis(N[0], 'F', domain=(-2*np.pi, 2*np.pi), dtype='D')
K1 = Basis(N[1], 'F', domain=(-2*np.pi, 2*np.pi), dtype='D')
K2 = Basis(N[2], 'F', domain=(-2*np.pi, 2*np.pi), dtype='d')
T = TensorProductSpace(comm, (K0, K1, K2), slab=False,
                       **{'planner_effort': 'FFTW_MEASURE'})

TT = MixedTensorProductSpace([T, T])

X = T.local_mesh(True)
uf = Array(TT)
u, f = uf[:]
up = Array(T)
duf = Function(TT)
du, df = duf[:]

uf_hat = Function(TT)
uf_hat0 = Function(TT)
uf_hat1 = Function(TT)
w0 = Function(T)
u_hat, f_hat = uf_hat[:]

# initialize (f initialized to zero, so all set)
u[:] = ul(*X)
u_hat = T.forward(u, u_hat)

uh = TrialFunction(T)
vh = TestFunction(T)
k2 = -inner(grad(vh), grad(uh)).scale - gamma

count = 0
def compute_rhs(duf_hat, uf_hat, up, T, w0):
    global count
    count += 1
    u_hat, f_hat = uf_hat[:]
    du_hat, df_hat = duf_hat[:]
    df_hat[:] = k2*u_hat
    up = T.backward(u_hat, up)
    df_hat += T.forward(gamma*up**3, w0)
    du_hat[:] = f_hat
    return duf_hat

def energy_fourier(comm, a):
    result = 2*np.sum(abs(a[..., 1:-1])**2) + np.sum(abs(a[..., 0])**2) + np.sum(abs(a[..., -1])**2)
    result =  comm.allreduce(result)
    return result

# Integrate using a 4th order Rung-Kutta method
a = [1./6., 1./3., 1./3., 1./6.]         # Runge-Kutta parameter
b = [0.5, 0.5, 1.]                       # Runge-Kutta parameter
t = 0.0
dt = 0.005
end_time = 1.
tstep = 0
if rank == 0:
    image = plt.contourf(X[1][..., 0], X[0][..., 0], u[..., 16], 100)
t0 = time()
K = np.array(T.local_wavenumbers(True, True, True))
TV = VectorTensorProductSpace([T, T, T])
gradu = Array(TV)
while t < end_time-1e-8:
    t += dt
    tstep += 1
    uf_hat1[:] = uf_hat0[:] = uf_hat
    for rk in range(4):
        duf = compute_rhs(duf, uf_hat, up, T, w0)
        if rk < 3:
            uf_hat[:] = uf_hat0 + b[rk]*dt*duf
        uf_hat1 += a[rk]*dt*duf
    uf_hat[:] = uf_hat1

    if tstep % 100 == 0:
        uf = TT.backward(uf_hat, uf)
        ekin = 0.5*energy_fourier(T.comm, f_hat)
        es = 0.5*energy_fourier(T.comm, 1j*K*u_hat)
        eg = gamma*np.sum(0.5*u**2 - 0.25*u**4)/
        eg =  comm.allreduce(eg)
        gradu = TV.backward(1j*K*u_hat, gradu)
        ep = comm.allreduce(np.sum(f*gradu)/
        ea = comm.allreduce(np.sum(np.array(X)*(0.5*f**2 + 0.5*gradu**2
                            - (0.5*u**2 - 0.25*u**4)*f))/
        if rank == 0:
  [1][..., 0], X[0][..., 0], u[..., 16], 100)
            plt.savefig('Klein_Gordon_{}_real_{}.png'.format(N[0], tstep))
            print("Time = %2.2f Total energy = %2.8e Linear momentum %2.8e Angular momentum %2.8e" %(t, ekin+es+eg, ep, ea))

print("Time ", time()-t0)


Ambrish Pandey, Janet D. Scheel, and Jörg Schumacher. Turbulent superstructures in rayleigh-bénard convection. Nature Communications, 9(1):2118, 2018. doi:10.1038/s41467-018-04478-0.