标题： 基于神经网络的Woodcock粒子追踪方法优化 显示英文标题

标题： Optimization of the Woodcock Particle Tracking Method Using Neural Network

摘要： Woodcock跟踪算法的接受率被推广为一个任意的位置相关变量$q(x)$。 使用神经网络来优化$q(x)$，FOM值用作损失函数。 这个想法来源于物理信息神经网络（PINN），其中使用神经网络来表示微分方程的解。 在这里，神经网络$q(x)$应该解决优化FOM的功能方程。 对于具有高斯吸收截面的一维传输问题，与常数$q$情况和原始Woodcock方法相比，FOM值有显著提高。 将神经网络Woodcock（NNW）方法推广到三维体素模型还有待探索。 显示英文摘要

摘要： The acceptance rate in Woodcock tracking algorithm is generalized to an arbitrary position-dependent variable $q(x)$. A neural network is used to optimize $q(x)$, and the FOM value is used as the loss function. This idea comes from physics informed neural network(PINN), where a neural network is used to represent the solution of differential equations. Here the neural network $q(x)$ should solve the functional equations that optimize FOM. For a 1d transmission problem with Gaussian absorption cross section, we observe a significant improvement of the FOM value compared to the constant $q$ case and the original Woodcock method. Generalizations of the neural network Woodcock(NNW) method to 3d voxel models are waiting to be explored.