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首页> 外文期刊>Communications in mathematical sciences >HYBRID DETERMINISTIC STOCHASTIC SYSTEMS WITH MICROSCOPIC LOOK-AHEAD DYNAMICS
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HYBRID DETERMINISTIC STOCHASTIC SYSTEMS WITH MICROSCOPIC LOOK-AHEAD DYNAMICS

机译:具有微观先行动力学的混合确定性随机系统

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We study the impact of stochastic mechanisms on a coupled hybrid system consisting of a general advection-diffusion-reaction partial differential equation and a spatially distributed stochastic lattice noise model. The stochastic dynamics include both spin-flip and spin-exchange type interparticle interactions. Furthermore, we consider a new, asymmetric, single exclusion process, studied elsewhere in the context of traffic flow modeling, with an one-sided interaction potential which imposes advective trends on the stochastic dynamics. This look-ahead stochastic mechanism is responsible for rich nonlinear behavior in solutions. Our approach relies heavily on first deriving approximate differential mesoscopic equations. These approximations become exact either in the long range, Kac interaction partial differential equation case, or, given sufficient time separation conditions, between the partial differential equation and the stochastic model giving rise to a stochastic averaging partial differential equation. Although these approximations can in some cases be crude, they can still give a first indication, via linearized stability analysis, of the interesting regimes for the stochastic model. Motivated by this linearized stability analysis we choose particular regimes where interacting nonlinear stochastic waves are responsible for phenomena such as random switching, convective instability, and metastability, all driven by stochasticity. Numerical kinetic Monte Carlo simulations of the coarse grained hybrid system are implemented to assist in producing solutions and understanding their behavior.
机译:我们研究了随机机制对耦合混合系统的影响,该混合系统由一般对流扩散反应偏微分方程和空间分布随机晶格噪声模型组成。随机动力学包括自旋翻转和自旋交换类型的粒子间相互作用。此外,我们考虑了一种新的,不对称的,单次排斥的过程,该过程在交通流建模的上下文中已在其他地方进行了研究,具有单方面的交互作用潜力,对随机动力学施加了对流趋势。这种超前的随机机制负责解决方案中的丰富非线性行为。我们的方法在很大程度上依赖于首先推导近似微分介观方程。这些近似在长距离Kac相互作用偏微分方程的情况下,或者在给定足够的时间分离条件的情况下,在偏微分方程和随机模型之间产生了随机平均的偏微分方程,这种近似变得精确。尽管这些近似值在某些情况下可能是粗略的,但它们仍可以通过线性稳定性分析给出有关随机模型有趣机制的第一个指示。受此线性化稳定性分析的激励,我们选择了特定的机制,其中相互作用的非线性随机波负责所有由随机性驱动的现象,例如随机切换,对流不稳定性和亚稳定性。实施了粗粒混合系统的数值动力学蒙特卡洛模拟,以帮助产生解决方案并理解其行为。

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