English(EN) Strong Averaging Principle and Long-Time Dynamics for Fast-Slow SDEs with Increasing Time-Scale Separation and Degenerate Noise

快慢随机微分方程退化噪声的新平均原理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

一篇新研究论文介绍了一种快慢随机微分方程（SDEs）的强平均原理。该原理适用于时间尺度分离随时间增加且噪声可能退化的系统。该方法依赖于冻结的快动力学的耗散性，允许退化扩散系数。该论文在后期建立了慢变量与平均常微分方程（ODE）之间的最大 L^p 估计，证明了经典强收敛率为 1/2。这项工作通过分析平均方程的动力学，为识别潜在的极限点和慢变量的渐近稳定平衡的收敛性提供了标准。 AI

排序理由该集群包含一篇发表在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=0.1]

在 arXiv stat.ML 阅读 →

arXiv

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arXiv stat.ML TIER_1 English(EN) · Sebastian Kassing, Asuto Miwa · 2026-08-25 04:00

具有递增时间尺度分离和退化噪声的快慢随机微分方程的强平均原理和长时间动力学

arXiv:2608.23462v1 Announce Type: cross Abstract: We establish a strong averaging principle for fast-slow stochastic differential equations with a time-dependent scale-separation parameter $(\varepsilon_t)_{t \geq 0}$ satisfying $\varepsilon_t \to 0$ as $t \to \infty$. In contras…

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具有递增时间尺度分离和退化噪声的快慢随机微分方程的强平均原理和长时间动力学

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