
doi: 10.1063/1.166223
pmid: 12779650
We propose a method to explore invariant measures of dynamical systems. The method is based on numerical tools which directly compute invariant sets using a subdivision technique, and invariant measures by a discretization of the Frobenius-Perron operator. Appropriate visualization tools help to analyze the numerical results and to understand important aspects of the underlying dynamics. This will be illustrated for examples provided by the Lorenz system.
invariant measures, Lorenz system, Functional analytic techniques in dynamical systems; zeta functions, (Ruelle-Frobenius) transfer operators, etc., Invariant measures for infinite-dimensional dissipative dynamical systems, Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.), Ergodicity, mixing, rates of mixing, invariant sets, Frobenius-Perron operator
invariant measures, Lorenz system, Functional analytic techniques in dynamical systems; zeta functions, (Ruelle-Frobenius) transfer operators, etc., Invariant measures for infinite-dimensional dissipative dynamical systems, Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.), Ergodicity, mixing, rates of mixing, invariant sets, Frobenius-Perron operator
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 46 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
