I'm trying to simulate the evolution of the Wigner function (a pseudo probability distribution over phase space) for a point particle moving in a chaotic potential. I'll provide background first, as well as some ignorant speculation at the end, but I've isolated my problem to a very simple case, so you should probably just skip to the "Reduced Problem".


The PDE governing the Wigner function $W(x,p,t)$ can be approximated as

$\partial_t W = -\frac{p}{m} \partial_x W + V^\prime (x) \partial_p W - \frac{\hbar^2}{24} V^{\prime \prime \prime} (x) \partial_p^3 W$

or, in the dimensionless PDE notation,

$W_t = -p W_x + f(x) W_p - f^{\prime \prime}(x) W_{ppp} .$

However, the problem I'm having occurs for zero potential. In this case, the $p$-dependance of the Wigner function decouples from it's evolutions, so we can just fix $p$.

Reduced Problem

Consider merely the equation

$\partial_t W = - c \ \partial_x W$

for $W(x,t)$. For initial conditions $W(x,t=0) = g(x)$, it is solved by $W(x,t) = g(x-ct)$, which is just the original wave sliding at speed $c$.

Suppose my initial condition is a normal distribution: $g(x) \propto e^{-x^2}$. If I were to simulate this with just Euler's method,

$W (x_i,t_{j+1}) = W (x_i,t_j)+ \Delta W (x_i,t_{j+1}), $

$\Delta W (x_i,t_j+1) = -c [W(x_{i+1})-W(x_{i-1})]/(2 \Delta x),$

I would quickly run into a serious instability: On the leading and trailing edges of the traveling Gaussian (where the function is concave up) Euler's method will under-estimate the time derivative, yielding overly steep leading and trailing edges. Here's an animated gif of what it looks like. Eventually, the trailing edge dips below zero and all hell breaks loose.

Furthermore, this instability scales poorly: $t_{breakdown} \propto \Delta t$, so if I want to double the length of the simulation it requires 4 times as many total time steps.

So, just use Heun's method or the 4th order Runge-Kutta method, right? Well, here's the thing. Switching to Heun's method does significantly improves the simulation; $t_{breakdown}$ increases by about a factor of 10. But, once the method is fixed (Heun, RK4, whatever), the scaling problem remains. This puts a pretty hard ceiling on the length of my simulation.

Now, when I use Heun's method, the breakdown is slightly different. Basically, the wave dips below zero on the distantly trailing tails of the Gaussian where the function is very small (like $10^{-40}$). But as soon as it does dip below zero, this error grows exponentially and swamps the simulation. Here is an animated gif zoomed in on the trailing edge on a logarithmic scale$^\dagger$.


Is there a way to fix this instability, or am I doomed to the same time scaling? Do I need to just keep increase the order of my Runge-Kutta method (RK5, RK6, etc.) in order to simulate for longer times?

(By the way, RK4 does not seem to improve much over Heun's method and take significantly longer to run.)

Ignorant Speculation

(Don't read...)

I think this has something to do with the nature of Gaussians. Euler's method breaks down because it calculates using the derivative at the beginning of the interval (which basically assumes the derivative is constant along the interval) so it performs badly for appreciable 2nd derivatives. Heun's method is better because it averages derivative at the beginning and end of the interval; it is vulnerable to strong third derivatives. I figure 4rth order Runge-Kutta is vulnerable to strong forth derivatives, although it's probably more complicated than that.

The problem with the Gaussian is that all derivatives (when normalized to the magnitude of the function) become large away from the center, so the evolution of the tails always breaks:

$g = e^{-x^2}$

$g^\prime/g = -2x$

$g^{\prime \prime}/g = 4x^2-2$

$g^{\prime \prime \prime}/g = -8x^3+12x$

But then again, it's not like Gaussians are unusal functions; if this were a problem, people would know about it and have a solution.

Also, the extreme tails of the Gaussian really shouldn't break the simulations. You think that if there were problems with derivatives for such tiny values then numerical errors introduced by machine precision would be worse.

$^\dagger$Actually, it's a crudely modified logarithmic scale which cuts out the range $[-10^{-50},10^{-50}]$ in order to displays negative values. I am about 50% sure that the apparent discontinuity of the "jump" as it falls below zero is due to crudeness of the scale. Here is the absolute value on a normal log scale. There does seem to be a weird "bounce" when it goes below zero.


Seeing as that I am getting errors to develop on the far tails of the Gaussian, I don't think adaptively adjusting will help. It doesn't seem like a special case/situation is causing the problem.

Also, I can't really check easily for trouble spots because I need to prevent this error even when I'm not in such a simple situation. Plus, the Wigner function is supposed to go negative occasionally, just not when it starts out positive and the equations of motion are so simple.


I don't think I can use an implicit method because, for non-trivial p-dependence, solving the implicit equation

$W(x_i,p_j,t_{k+1}) - W(x_i,p_j,t_k) -\Delta t \ f[W(x_1,p_j,t_{k+1})]=0$

for $W(x_i,p_j,t_{k+1})$ isn't computationally feasible because $W(x_i,p_j,t=t_0)$ is a huge 2 dimensional matrix. The Scholarpedia article by Hamdi et al (suggested by Jitse Niesen below) supports this.


3 Answers 3


What's the CFL condition of the method for your problem? This is the main thing missing from the formulation of the problem and many times at the heart of instabilities of the type you discuss. As the wikipedia site says, implicit method are usually better for maintaining a reasonable CFL condition.

The CFL condition will tell you the relationship between $\Delta t$ and $\Delta x$ that causes spurious oscillations to die down. By figuring this out in advance you can find a method that gives you a relationship that is better for your problem.

The standard way to find the CFL condition is to look for a wave solution $e^{\omega t + k\dot x}$, plug it into the numerical method and see what the numerical dispersion relation (between $\omega$ and $k$) is. The point is that for a $k$ that corresponds to an oscillation every $2\Delta x$ ($k=\pi/\Delta x$) you must have decay (meaning $Re(\omega)<0$, imaginary part not important).

The condition that the real part is negative is the CFL condition. Now, for your problem, wave solutions are probably not going to be solutions....because of the nonconstant f(x). so I would replace both by constants just to see how the sizes of f and f'' play in the CFL condition....

  • $\begingroup$ I think for my general problem, non-trivial p-dependence, implicit methods are impractical (although I might just be really confused). The implicit (backward) Euler method, for instance, requires solving $y_{k+1}−y_k−f(y_{k+1})=0$ for $y_{k+1}$. But in my case $y$ ~ $W$, which is a giant 2-dimensional matrix, so I don't think there's a feasible method for finding roots of the implicit equation. Or am I just unaware of implicit methods for PDEs? $\endgroup$ May 12, 2010 at 19:33
  • 1
    $\begingroup$ Looking at your edits, and reading the scolarpedia article, I want to add that the problem you are solving is not nonlinear. It is linear: if $W_1$ and $W_2$ are solutions then so is $\alpha W_1 + \beta W_2$. Thus, you will only need to invert that system once per timestep, and since the system you need to solve has the same matrix on the LHS you should be able to create an LU decomposition and then solve very quickly at each timestep. You sill need to use sparse-matrix data structure and methods, but any linear algebra package should have that functionality. $\endgroup$ May 13, 2010 at 8:27

When you say you use Heun's method, I assume you mean that you first use central differences on the spatial term in the PDE, yielding a system of ODEs: $$ \frac{d}{dt} W(x_i,t) = -c \frac{W(x_{i+1},t) - W(x_{i-1},t)}{2\Delta x}, $$ and then solve the ODEs with Heun's method. So your method looks like $$ W^*(x_i,t_{j+1}) = W(x_i,t_{j+1}) - c \Delta t \frac{W(x_{i+1},t_i) - W(x_{i-1},t_i)}{2\Delta x}, $$ $$ W(x_i,t_{j+1}) = W(x_i,t_{j+1}) - c \frac{\Delta t}{2} \left( \frac{W(x_{i+1},t_i) - W(x_{i-1},t_i)}{2\Delta x} + \frac{W^*(x_{i+1},t_i) - W^*(x_{i-1},t_i)}{2\Delta x} \right), $$ This is called the Method of Lines. Reading that article, and more generally the literature on this method, should give you some idea of what's going on.

I can only give you some hints. The system of ODEs is stiff with stiffness ratio proportional to $1/\Delta x$ (this is basically the CFL condition that yfarjoun talks about). Explicit methods like Euler's method thus have a step size restriction $\Delta t < C (\Delta x)$ with the constant $C$ depending on the method. This may be what you're seeing. The solution for this is to use an implicit method (or live with the step size restriction).

Another possibility: Your speculation suggests that you have problems with spurious oscillations. A solution for that is to use Total Variation Diminishing schemes.


There are a couple approaches that I think you could take to avoid this problem.

Consider your Euler's method example. With this example, you know that the value at the next time step $W(x_i,t_{j+1})$ will go negative if the delta term, $\frac{c\left[W(x_{i+1},t_{j+1}-W(x_{i-1},t_{j+1})\right]}{2\Delta x}$ is greater than the value at the current time step, $W(x_i,t_j)$. The negative value leads to error inflation, and so on.

First, you could try an adaptive solver, which varies the step size to meet certain error tolerances. MATLAB, for example, comes with ode45() which uses a 4th order and 5th order Runge-Kutta solver in conjunction with one another, and adaptively adjusts the step size.

A second solution is to use a multi-step method, such as an Adams-Bashforth method. These methods are well-suited for stiff problems, which although your particular issue is not due to stiffness, it does seem to suffer from the same issues as stiff problems -- that is, the method is incapable of approximating the derivative of the function within a desired error tolerance within a neighborhood of a set of points.

A third solution is a hybrid approach. Since you know how to evaluate, based on your chosen ODE solver, when the next step will go negative, then you could put in place some conditionals that changes the routine when you encounter these trouble spots either by switching to a different method, or reducing the step size in some ad hoc manner. Alternatively, you could switch to a multi-step method at this point, and use the preceding $m$ steps as the seed for the multi-step method.

I haven't put a whole lot of effort into evaluating the region of instability, since I don't know how well the reduced problem applies to your current needs, but if you substituted $e^{(x-ct)^2}$ in your delta term for Euler's method, you could probably determine pretty easily when your solution will dip negative.

  • $\begingroup$ Thanks very much for the thoughtful reply. I'll have to edit the question to fit in my thoughts. I will definitely look into the Adams-Bashforth and other multi-step methods. Thanks again. $\endgroup$ May 12, 2010 at 1:35
  • $\begingroup$ Another thought... you gain some marginal stability by increasing the order of your solver. This gain is analogous to reducing the step size of a lower order solver (by a very large margin). Does changing \Delta x have any effect on your system? $\endgroup$ May 13, 2010 at 16:22

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