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Accurate and robust splitting methods for the generalized Langevin equation with a positive Prony series memory kernel

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 Added by Xiaocheng Shang
 Publication date 2021
  fields Physics
and research's language is English




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We study numerical methods for the generalized Langevin equation (GLE) with a positive Prony series memory kernel, in which case the GLE can be written in an extended variable Markovian formalism. We propose a new splitting method that is easy to implement and is able to substantially improve the accuracy and robustness of GLE simulations in a wide range of the parameters. An error analysis is performed in the case of a one-dimensional harmonic oscillator, revealing that all but one averages are exact for the newly proposed method. Various numerical experiments in both equilibrium and nonequilibrium simulations are also conducted to demonstrate the superiority of the newly proposed method over popular alternative schemes in interacting multi-particle systems.



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