Data-driven parameterization of the generalized Langevin equation

Huan Lei, Nathan A. Baker, Xiantao Li

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuationdissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)14183-14188
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number50
StatePublished - Dec 13 2016

All Science Journal Classification (ASJC) codes

  • General


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