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arXiv 2309.08366math.PRmath.DS
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G-布朗运动驱动的随机微分方程的不变性原理及其在G-随机控制中的应用

Invariance principles for G-brownian-motion-driven stochastic differential equations and their applications to G-stochastic control

Xiaoxiao Peng, Shijie Zhou, Wei Lin, Xuerong Mao

AI总结:

本文建立新的G-半鞅收敛定理与不变性原理,证明局部Lipschitz且具线性上界条件下G-SDE解的全局存在唯一性,并应用于代表性动力系统的G-随机控制。

摘要:

由Peng及其同事开创性提出的G-布朗运动驱动随机微分方程(G-SDE)以及G-期望,已被广泛应用于描述现实世界系统建模中出现的一类特殊不确定性。从数学上刻画G-SDE所产生解的长时间行为和极限行为,有助于理解系统演化的机制。在此,我们发展了一个新的G-半鞅收敛定理,并进一步建立了一个新的不变性原理,用于研究G-SDE中出现的长时间行为。我们还验证了向量场仅为局部Lipschitz且具有线性上界的G-SDE解的唯一性和全局存在性。为证明我们通过分析建立的结果的广泛适用性,我们研究了其在若干代表性动力系统中实现G-随机控制的应用。

英文摘要:

The G-Brownian-motion-driven stochastic differential equations (G-SDEs) as well as the G-expectation, which were seminally proposed by Peng and his colleagues, have been extensively applied to describing a particular kind of uncertainty arising in real-world systems modeling. Mathematically depicting long-time and limit behaviors of the solution produced by G-SDEs is beneficial to understanding the mechanisms of system's evolution. Here, we develop a new G-semimartingale convergence theorem and further establish a new invariance principle for investigating the long-time behaviors emergent in G-SDEs. We also validate the uniqueness and the global existence of the solution of G-SDEs whose vector fields are only locally Lipchitzian with a linear upper bound. To demonstrate the broad applicability of our analytically established results, we investigate its application to achieving G-stochastic control in a few representative dynamical systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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