Closure problems are omnipresent when simulating multiscale systems, where some
quantities and processes cannot be fully prescribed despite their effects on the simulation's …

We introduce a method to accurately and efficiently estimate the effective dynamics of
collective variables in molecular simulations. Such reduced dynamics play an essential role …

Abstract Model reduction methods aim to describe complex dynamic phenomena using only
relevant dynamical variables, decreasing computational cost, and potentially highlighting …

We introduce a machine-learning-based coarse-grained molecular dynamics model that
faithfully retains the many-body nature of the intermolecular dissipative interactions. Unlike …

We present data-driven coarse-grained (CG) modeling for polymers in solution, which
conserves the dynamic as well as structural properties of the underlying atomistic system …

One important problem in constructing the reduced dynamics of molecular systems is the
accurate modeling of the non-Markovian behavior arising from the dynamics of unresolved …

We present a data-driven method to learn stochastic reduced models of complex systems
that retain a state-dependent memory beyond the standard generalized Langevin equation …

The Mori--Zwanzig formalism is applied to derive an equation for the evolution of linear
observables of the overdamped Langevin equation. To illustrate the resulting equation and …

An introductory, pedagogical review of the generalized Langevin equation (GLE) within the
classical regime is presented. It is intended to be accessible to biophysicists with an interest …

In this paper, we introduce a data-driven modeling approach for dynamics problems with
latent variables. The state-space of the proposed model includes artificial latent variables, in …