Slow dynamical modes from static averages

08 November 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In recent times, efforts are being made at describing the evolution of a complex system not through long trajectories, but via the study of probability distribution evolution. This more collective approach can be made practical using the transfer operator formalism and its associated dynamics generator. Here, we reformulate in more transparent way the result of [Devergne et al. arXiv:2406.09028] and show that the lowest eigenfunctions and eigenvalues of the dynamics generator can be efficiently computed using data easily obtainable from biased simulations. We also show explicitly that the long time dynamics can be reconstructed by using the spectral decomposition of the dynamics operator

Keywords

Slow modes
Dynamics
Transfer operator
neural network
enhanced sampling
machine learning CV

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