2020/07/13 by Sajjad Azimi, Azimi, Sajjad, Omid Ashtari +3
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Neural Networks and Reservoir Computing #Meteorological Phenomena and Simulations #Chaos control and synchronization
paper · pdf · doi:10.48550/arxiv.2007.06427
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.