Improving the prediction of chaotic time series

One of the features of deterministic chaos is sensitive to initial conditions. This feature limits the prediction horizons of many chaotic systems. In this paper, we propose a new prediction technique for chaotic time series. In our method, some neighbouring points of the predicted point, for which the corresponding local Lyapunov exponent is particularly large, would be discarded during estimating the local dynamics, and thus the error accumulated by the prediction algorithm is reduced. The model is tested for the convection amplitude of Lorenz systems. The simulation results indicate that the prediction technique can improve the prediction of chaotic time series.     

SU(2)×SU(2)格点手征模型的进一步研究

本文利用变分─累积展开方法将SU(2)×SU(2)格点手征模型的自由能展开到第五级,分别用主值法和聚点法(accumulation point)确定变分参数,结果表明后者能得到与蒙特卡罗模拟更为一致的内能和比热曲线.     

变分累积展开方法中确定相变点的新方法

在计算有限温度SU(2)格点规范模型Polyakov线的过程中,考虑到变分累积展开法中有限距离相互作用效应与Monte Carlo中的有限体积效应的相似性,采用了新的方法来确定相变点.与已有的方法以及Monte Carlo模拟给出的结果比较,新方法给出的结果更接近Monte Carlo结果.