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The reduction of potential diffusions to finite state Markov chains and stochastic resonance

机译:减少有限状态马尔可夫链和随机共振的潜在扩散

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Consider a dynamical system describing the motion of a particle in a double well potential with a periodic perturbation of very small frequency, and a white noise perturbation of intensity ε. If its trajectories amplify the small periodic perturbation in a 'best possible way', it is said to be in stochastic resonance. A lower bound for the ratio of amplitude and logarilhm of the period above which quasi-deterministic periodic behavior can be observed is obtained via large deviations theory. However, to obtain optimality, periodicity of trajectories has to be studied by means of a measure of quality of tuning such as spectral power amplification. In the particular setting where the potential alternates every half period between two spatially antisymmetric double well states we encounter a surprise. The stochastic resonance pattern is not correctly described by the reduced dynamics associated with a two state Markov chain whose periodic hopping rates between the potential minima mimic the large (spatial) scale motion of the diffusion. Only if small scale fluctuations inside the potential wells where the diffusion spends most of its time are carefully eliminated, the reduced dynamics is robust.
机译:考虑一种动态系统,其描述粒子在双井电位中具有非常小的频率的周期性扰动,以及强度ε的白色噪声扰动。如果其轨迹以“最佳方式”放大小定期扰动,则据说据说是随机共振。通过大的偏差理论获得高于上述时间的幅度和对数和对伐木比率的较低限制。然而,为了获得最优性,必须通过诸如光谱功率放大的调谐质量的量度来研究轨迹的周期性。在特定的设置中,在两个空间反对手双重井中的潜在每半个时间遇到惊喜。随机谐振模式未通过与两个状态马尔可夫链相关联的动态来正确描述,其周期跳率与潜在的最小值之间的周期跳率模拟扩散的大(空间)比例运动。只有在潜在的井内的小规模波动时,才会仔细地消除其中大部分时间的潜在井,才能仔细地消除,减少的动态是强大的。

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