# Data-driven learning of the generalized Langevin equation with state-dependent memory

Source: [Data-driven learning of the generalized Langevin equation with state-dependent memory](<https://www.sota2.com/research/paper/68fa458d1e209131ce91975b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T04:25:10.449Z

Authors: Pei Ge, Zhongqiang Zhang, Huan Lei

Published: 2023-10-28T04:10:39.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.18582>)

[PDF](<https://arxiv.org/pdf/2310.18582.pdf>)