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A completely different way of approximating a function is to use a statistical sampling with quasi-particles
where is the weight, the shape, and the position of each particle. In the JBONE applet, the particle shapes are assumed to be Dirac pulses , meaning that the particles are localized in an infinitely small interval around
For simplicity, the weight is often set to unity . As a matter of fact, this form of discretization does never converge locally, since the Dirac pulses are either zero or infinite. However, the ``global properties'' (or moments) of a smooth and bounded function discretized with Dirac pulses does converge
Without a local convergence, it is of course difficult to compare a quasi-particle discretization with a different approximation, based for example on finite elements. The particle solution is therefore projected onto a set of basis functions in an assignment process, which attributes a statistical average to a grid:
In JBONE, the projecting basis functions are piecewise linear roof-top functions. Note that it is important to use the same set of functions for the projection and the plot: figure (1.4.7#fig.1) shows how a Gaussian particle distribution appears after projection, when the result is plotted using piecewise constant (box) and linear (roof-top) functions. The dashed line incorrectly mixes boxes for the projection with roof-tops for the plot.
Try to press INITIALIZE a few times to get a feeling how good a quasi-particle approximation is to approximate a box with 64 grid points and a varying number of particles (random walkers). You need however to be patient if you exceed particles... the JAVA virtual machines in the web browsers are usually rather slow!
The applet uses random numbers to generate a particle distribution from the initial condition with a range in the interval following the procedure
LetThis algorithm produces a particle density in the interval that is proportional to the initial condition .
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