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- W4320854964 abstract "Coarse-graining or model reduction of dynamical systems is a tool used to extend the time-scale of simulations in a range of fields. When applied in molecular dynamics, standard coarse-graining approaches seek to approximate the potential of mean force, which is the expected potential energy conditioned on the value of the coarse-grained variables, and use this to drive an effective Markovian model. Here, in an overdamped setting, the simple case of a quadratic energy is studied. It is shown that while the standard recipe for model reduction accurately captures equilibrium statistics, it is possible to derive an easy-to-implement Markovian approach to better capture dynamical statistics, such as the mean-squared displacement. In particular, the dependence of the error in the dynamical autocovariance predicted by our reduced model is quantified in terms of both the time-scale separation in the energy and the choice of coarse-grained variables. Both analytical and numerical evidence for the efficacy of the approach is provided." @default.
- W4320854964 created "2023-02-16" @default.
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- W4320854964 creator A5060105528 @default.
- W4320854964 date "2023-02-13" @default.
- W4320854964 modified "2023-09-29" @default.
- W4320854964 title "Dynamical properties of coarse-grained linear SDEs" @default.
- W4320854964 doi "https://doi.org/10.48550/arxiv.2302.06535" @default.
- W4320854964 hasPublicationYear "2023" @default.
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