title: Data-driven parameterization of the generalized Langevin equation.
abstract: 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 fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.
authors.affiliation: Advanced Computing, Mathematics & Data, Pacific Northwest National Laboratory, Richland, WA 99352; huan.lei@pnnl.gov.
authors.forename: Huan
authors.initials: H
authors.lastname: Lei
authors.affiliation: Advanced Computing, Mathematics & Data, Pacific Northwest National Laboratory, Richland, WA 99352.|Division of Applied Mathematics, Brown University, Providence, RI 02912.
authors.forename: Nathan A
authors.initials: NA
authors.lastname: Baker
authors.affiliation: Department of Mathematics, The Pennsylvania State University, University Park, PA 16802.
authors.forename: Xiantao
authors.initials: X
authors.lastname: Li
keywords: coarse-grained molecular models; data-driven parameterization; generalized Langevin dynamics; model reduction; reaction rate