Is there a theory for solving PDE by using Cellular Automata ? Something which is on the line of, passing to the limit (scale) i.e., if you increase the number of grid points the solution to the cellular automata will converge to the PDE ? If so, how successful is this approach ? What are the limitations of this approach. Also, given a PDE how does one go about finding the rules for the corresponding cellular automata and vice versa ?
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There are a number of PDEs that have been fruitfully attacked using cellular automata. First among these are various incarnations of the Navier-Stokes equations, which can be (and in geophysical or other complex flow applications, frequently are) simulated with lattice gases and lattice Boltzmann methods. Other PDEs that CAs can handle include diffusion and reaction-diffusion equations and wave equations. Another one that is more widely known is the random walk treated as a random CA, which can be used to tackle the heat equation. Will Jagy's guess about hexagons anticipates (if we think about triangles instead) the improvement that so-called FHP models offer over HPP models; in higher dimensions the lattice issues get trickier. I have some unpublished and cryptic notes about a possible new approach in 3D using the root lattice $A_4$ and permutohedral boundary conditions. One of the main advantages of the CA approach is the ability to work with complicated boundary shapes, though on the other hand the boundary conditions are a very delicate issue in general. A very nice (though somewhat dated) reference for this and related problems is Chopard and Droz (a PDF of the opening parts is here) and IIRC you can find a paper by one of the authors online that covers some of the same topics with a similar approach. |
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The only one I know may not count as an automaton. For a two dimensional Dirichlet problem for the Laplacian, take a square grid. Fix the values of the function on the squares judged to be boundary squares. Fill in some values in all the middle squares. At each stage, the value at a square becomes the average of the values at the four immediately neighboring squares, up, down, left, right. This is a discrete analog of the mean value property of harmonic functions. I don't know how well this method is regarded. I also suspect one would get a better mean value property with a grid of hexagons. However, in larger dimension we are probably stuck with cubes. |
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