论文标题
蒙特 - 卡洛算法中稀有事件采样的瞬间保存和网状自适应重新加权方法
Moment-Preserving and Mesh-Adaptive Reweighting Method for Rare-Event Sampling in Monte-Carlo Algorithms
论文作者
论文摘要
我们提出了新颖的轮盘方案，用于稀有事件采样，既具有结构性且无偏见。蒙特卡洛标记物被拆分和删除的边界自动放置并在运行时进行调整。可以在没有重大变化的情况下使用新方案扩展现有代码，因为标记的运动方程式不会改变。这些方案也可以在标记之间的非线性和非局部耦合的情况下应用。作为说明性应用，我们已经在Ascot-RFOF代码中实现了此方法，用于通过融合等离子体中的射频波来模拟快速离子生成。在此应用中，使用此方法，典型快速离子能量的蒙特卡罗噪声水平至少可以在一个数量级中降低，而无需增加计算工作。
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. These schemes can also be applied in the presence of nonlinear and nonlocal coupling between markers. As an illustrative application, we have implemented this method in the ASCOT-RFOF code, used to simulate fast-ion generation by radio-frequency waves in fusion plasmas. In this application, with this method the Monte-Carlo noise level for typical fast-ion energies can be reduced at least of one order of magnitude without increasing the computational effort.
