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arXiv 0704.0022math.NAcs.NA
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随机李群积分器

Stochastic Lie group integrators

Heriot-Watt University(赫瑞瓦特大学)

Simon J. A. Malham, Anke Wiese

AI总结:

针对解在光滑有限维流形上演化的非线性随机微分方程,提出基于李群作用的随机Munthe-Kaas方法和Castell-Gaines方法,确保数值解位于流形上,并证明部分Castell-Gaines方法比随机Taylor格式更精确。

摘要:

我们提出了用于非线性随机微分方程的李群积分器,这些方程具有非交换向量场,其解在光滑有限维流形上演化。给定一个生成沿流形输运的李群作用,我们通过该作用将流形上的随机流拉回到李群,然后通过指数映射将流进一步拉回到相应的李代数。我们通过封闭运算在李代数中构造随机流的近似,然后推回到李群,再推回到流形,从而确保我们的近似位于流形上。我们将此类格式称为随机Munthe-Kaas方法,以对应其确定性版本。我们还提出了基于Castell-Gaines方法的随机李群积分格式。这些方法涉及使用一个底层常微分积分器来近似由截断的随机指数李级数生成的流。如果使用Munthe-Kaas方法作为底层常微分积分器,它们就变成了随机李群积分器格式。此外,我们证明了一些Castell-Gaines方法比相应的随机Taylor格式具有更高的精度。最后,我们通过模拟受两个独立乘性随机噪声过程扰动的自由刚体(如卫星和自主水下航行器)的动力学来演示我们的方法。

英文摘要:

We present Lie group integrators for nonlinear stochastic differential equations with non-commutative vector fields whose solution evolves on a smooth finite dimensional manifold. Given a Lie group action that generates transport along the manifold, we pull back the stochastic flow on the manifold to the Lie group via the action, and subsequently pull back the flow to the corresponding Lie algebra via the exponential map. We construct an approximation to the stochastic flow in the Lie algebra via closed operations and then push back to the Lie group and then to the manifold, thus ensuring our approximation lies in the manifold. We call such schemes stochastic Munthe-Kaas methods after their deterministic counterparts. We also present stochastic Lie group integration schemes based on Castell--Gaines methods. These involve using an underlying ordinary differential integrator to approximate the flow generated by a truncated stochastic exponential Lie series. They become stochastic Lie group integrator schemes if we use Munthe-Kaas methods as the underlying ordinary differential integrator. Further, we show that some Castell--Gaines methods are uniformly more accurate than the corresponding stochastic Taylor schemes. Lastly we demonstrate our methods by simulating the dynamics of a free rigid body such as a satellite and an autonomous underwater vehicle both perturbed by two independent multiplicative stochastic noise processes.

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