Moment-Preserving and Mesh-Adaptive Reweighting Method for Rare-Event Sampling in Monte-Carlo Algorithms
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We present novel roulette schemes for rare-event sampling that are both structure-preserving and unbiased. The boundaries where Monte Carlo markers are split and deleted are placed automatically and adapted during runtime. Extending existing codes with the new schemes is possible without severe changes because the equation of motion for the markers is not altered. A nonlinear and nonlocal coupling between markers, as in the case of ion cyclotron resonance heating, is permitted. We show results from the ASCOT-RFOF code as an application of the schemes. The amplitude of Monte Carlo noise for 2 MeV ions can be reduced by a factor of 18, what would normally take over 350 times as many markers.
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