Coarse-graining Langevin dynamics using reduced-order techniques

Abstract

This paper considers the reduction of the Langevin equation arising from bio-molecular models. To facilitate the construction and implementation of the reduced models, the problem is formulated as a reduced-order modeling problem. The reduced models can then be directly obtained from a Galerkin projection to appropriately defined Krylov subspaces. The equivalence to a moment-matching procedure, previously implemented in , 2), is proved. A particular emphasis is placed on the reduction of the stochastic noise, which is absent in many order-reduction problems. In particular, for order less than six we can show the reduced model obtained from the subspace projection automatically satisfies the fluctuation-dissipation theorem. Details for the implementations, including a bi-orthogonalization procedure and the minimization of the number of matrix multiplications, will be discussed as well.

References in corpus (3)

• Data-driven parameterization of the generalized Langevin equation
• Normal form transforms separate slow and fast modes in stochastic dynamical systems
• The Derivation and Approximation of Coarse-grained Dynamics from Langevin Dynamics

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