Sensitivity analysis in chaotic dynamical systems is a challenging task from a computational point of view.In this work,we present a numerical investigation of a novel approach,known as the space-split sensitivity or ...Sensitivity analysis in chaotic dynamical systems is a challenging task from a computational point of view.In this work,we present a numerical investigation of a novel approach,known as the space-split sensitivity or S3 algorithm.The S3 algorithm is an ergodic-averaging method to differentiate statistics in ergodic,chaotic systems,rigorously based on the theory of hyperbolic dynamics.We illustrate S3 on one-dimensional chaotic maps,revealing its computational advantage over na?ve finite difference computations of the same statistical response.In addition,we provide an intuitive explanation of the key components of the S3 algorithm,including the density gradient function.展开更多
基金supported by the Air Force Office of Scientific Research(Grant FA8650-19-C-2207)。
文摘Sensitivity analysis in chaotic dynamical systems is a challenging task from a computational point of view.In this work,we present a numerical investigation of a novel approach,known as the space-split sensitivity or S3 algorithm.The S3 algorithm is an ergodic-averaging method to differentiate statistics in ergodic,chaotic systems,rigorously based on the theory of hyperbolic dynamics.We illustrate S3 on one-dimensional chaotic maps,revealing its computational advantage over na?ve finite difference computations of the same statistical response.In addition,we provide an intuitive explanation of the key components of the S3 algorithm,including the density gradient function.
