Slow dynamical modes from static averages

doi:10.1063/5.0246248

Slow dynamical modes from static averages

2025-03-28

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DOI：10.1063/5.0246248

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In recent times, efforts have been made to describe 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 a more transparent way the result of Devergne et al. [Adv. Neural Inform. Process. Syst. 37, 75495-75521 (2024)] 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.

Slow dynamical modes from static averages

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