Understanding dynamics in complex systems is challenging because there are many degrees of freedom, and those that are most important for describing events of interest are often not obvious. The leading eigenfunctions of the transition operator are useful for visualization, and they can provide an efficient basis for computing statistics, such as the likelihood and average time of events (predictions). Here, we develop inexact iterative linear algebra methods for computing these eigenfunctions (spectral estimation) and making predictions from a dataset of short trajectories sampled at finite intervals. We demonstrate the methods on a low-dimensional model that facilitates visualization and a high-dimensional model of a biomolecular system. Implications for the prediction problem in reinforcement learning are discussed.
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction
John Strahan, Spencer C. Guo, Chatipat Lorpaiboon, Aaron R. Dinner, Jonathan Weare; Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction. J. Chem. Phys. 7 July 2023; 159 (1): 014110. https://doi.org/10.1063/5.0151309
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