60J20 Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) [See also 90B30, 91D10, 91D35, 91E40]
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- Frobenius Perron operator (1)
- Koopman operator (1)
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- Perron cluster (1)
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- biochemical conformations (1)
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As has been shown recently, the identification of metastable chemical conformations leads to a Perron cluster eigenvalue problem for a reversible Markov operator. Naive discretization of this operator would suffer from combinatorial explosion. As a first remedy, a pre-identification of essential degrees of freedom out of the set of torsion angles had been applied up to now. The present paper suggests a different approach based on neural networks: its idea is to discretize the Markov operator via self-organizing (box) maps. The thus obtained box discretization then serves as a prerequisite for the subsequent Perron cluster analysis. Moreover, this approach also permits exploitation of additional structure within embedded simulations. As it turns out, the new method is fully automatic and efficient also in the treatment of biomolecules. This is exemplified by numerical results.
The statistical behavior of deterministic and stochastic dynamical systems may be described using transfer operators, which generalize the notion of Frobenius Perron and Koopman operators. Since numerical techniques to analyze dynamical systems based on eigenvalues problems for the corresponding transfer operator have emerged, bounds on its essential spectral radius became of interest. This article shows that they are also of great theoretical interest. We give an analytical representation of the essential spectral radius in $L^{1}\!(\mu)$, which then is exploited to analyze the asymptotical properties of transfer operators by combining results from functional analysis, Markov operators and Markov chain theory. In particular, it is shown, that an essential spectral radius less than $1$, constrictiveness and some weak form'' of the so--called Doeblin condition are equivalent. Finally, we apply the results to study three main problem classes: deterministic systems, stochastically perturbed deterministic systems and stochastic systems.