论文标题
具有非平稳记忆内核的广义Langevin动力学模拟：如何发出噪声
Generalized Langevin dynamics simulation with non-stationary memory kernels: How to make noise
论文作者
论文摘要
我们提出了一种数值方法，该方法根据具有非平稳内存内核的广义Langevin方程生成随机动力学。当具有明显时间依赖性的liouvillian的微观系统通过投影操作员形式主义而粗粒时，就会发生这种动力学。我们展示了如何通过随机过程替换广义Langevin方程中的确定性波动力，从而使可观察物的分布在给定顺序的矩中复制。因此，结合一种从基础微观模型的仿真数据中提取内存内核的方法，此处介绍的方法允许构建和模拟驱动过程的粗粒模型。
We present a numerical method to produce stochastic dynamics according to the generalized Langevin equation with a non-stationary memory kernel. This type of dynamics occurs when a microscopic system with an explicitly time-dependent Liouvillian is coarse-grained by means of a projection operator formalism. We show how to replace the deterministic fluctuating force in the generalized Langevin equation by a stochastic process, such that the distributions of the observables are reproduced up to moments of a given order. Thus, in combination with a method to extract the memory kernel from simulation data of the underlying microscopic model, the method introduced here allows to construct and simulate a coarse-grained model for a driven process.
