Solving the master equation without kinetic Monte Carlo: Tensor train approximations for a CO oxidation model

  • In multiscale modeling of heterogeneous catalytic processes, one crucial point is the solution of a Markovian master equation describing the stochastic reaction kinetics. Usually, this is too high-dimensional to be solved with standard numerical techniques and one has to rely on sampling approaches based on the kinetic Monte Carlo method. In this study we break the curse of dimensionality for the direct solution of the Markovian master equation by exploiting the Tensor Train Format for this purpose. The performance of the approach is demonstrated on a first principles based, reduced model for the CO oxidation on the RuO2(110) surface. We investigate the complexity for increasing system size and for various reaction conditions. The advantage over the stochastic simulation approach is illustrated by a problem with increased

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Metadaten
Author:Patrick Gelß, Sebastian Matera, Christof Schütte
Document Type:Article
Parent Title (English):Journal of Computational Physics
Volume:314
First Page:489
Last Page:502
Date of first Publication:2016/06/01
DOI:http://dx.doi.org/10.1016/j.jcp.2016.03.025

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