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 (GLE) with a homogeneous kernel. The constructed model naturally encodes the heterogeneous energy dissipation by jointly learning a set of state features and the non-Markovian coupling among the features. Numerical results demonstrate the limitation of the standard GLE and the essential role of the broadly overlooked state-dependency nature in predicting molecule kinetics related to conformation relaxation and transition.
@article{arxiv.2310.18582,
title = {Data-driven learning of the generalized Langevin equation with state-dependent memory},
author = {Pei Ge and Zhongqiang Zhang and Huan Lei},
journal= {arXiv preprint arXiv:2310.18582},
year = {2023}
}