Abstract
The Monte-Carlo method applied to transport theory appears as a mixture of measure theory and random numbers sampling. The idea that we shall develop is to keep measure theory and to give up random numbers.
More precisely, in the Monte-Carlo method, sources are represented by a finite number of ‘particles’, i.e. Dirac measures, which eventually follow the characteristic lines of the problem to be solved.
In the standard method, positions, directions and energies of these particles are determined via random number generation. In our method instead, we choose a mesh for the phase space and a fixed number of points in each cell, and we generate exactly one particle at each of these points.
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Mercier, B. (1985). Non statistical Monte-Carlo. In: Alcouffe, R., Dautray, R., Forster, A., Ledanois, G., Mercier, B. (eds) Monte-Carlo Methods and Applications in Neutronics, Photonics and Statistical Physics. Lecture Notes in Physics, vol 240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0049060
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DOI: https://doi.org/10.1007/BFb0049060
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