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arXiv 2007.06427nlin.CDphysics.flu-dyn
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构造高维混沌系统周期轨道的伴随变分方法

Adjoint-based variational method for constructing periodic orbits of high-dimensional chaotic systems

Sajjad Azimi, Omid Ashtari, Tobias M. Schneider

摘要:

从低维非线性微分方程到包含流体湍流的高维时空系统的系统中的混沌动力学由控制方程的非混沌、精确重现的时间周期解支持。这些不稳定的周期性轨道捕获的湍流动力学的关键特征和足够大的轨道集承诺的框架来预测混沌流的统计。计算高维时空混沌系统的周期轨道仍然具有挑战性,因为已知的方法要么表现出较差的收敛性能,因为它们是基于时间推进的混沌系统,导致指数误差放大;或者它们需要构造雅可比矩阵,这是非常昂贵的。我们提出了一种新的无矩阵方法,不受指数误差放大的影响,是全局收敛的,可以应用于高维系统。基于伴随的变分方法在闭环空间中构造了一个初值问题,使得周期轨道吸引回路动力学的不动点。我们介绍了一般自治系统的方法。一维Kuramoto-Sivashinsky方程的实现证明了时空混沌下周期轨道的鲁棒收敛性。收敛不需要精确的初始猜测,并且与各自轨道的周期无关。

英文摘要:

Chaotic dynamics in systems ranging from low-dimensional nonlinear differential equations to high-dimensional spatio-temporal systems including fluid turbulence is supported by non-chaotic, exactly recurring time-periodic solutions of the governing equations. These unstable periodic orbits capture key features of the turbulent dynamics and sufficiently large sets of orbits promise a framework to predict the statistics of the chaotic flow. Computing periodic orbits for high-dimensional spatio-temporally chaotic systems remains challenging as known methods either show poor convergence properties because they are based on time-marching of a chaotic system causing exponential error amplification; or they require constructing Jacobian matrices which is prohibitively expensive. We propose a new matrix-free method that is unaffected by exponential error amplification, is globally convergent and can be applied to high-dimensional systems. The adjoint-based variational method constructs an initial value problem in the space of closed loops such that periodic orbits are attracting fixed points for the loop-dynamics. We introduce the method for general autonomous systems. An implementation for the one-dimensional Kuramoto-Sivashinsky equation demonstrates the robust convergence of periodic orbits underlying spatio-temporal chaos. Convergence does not require accurate initial guesses and is independent of the period of the respective orbit.

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