NEW preprint out on Koopman Learning with Episodic Memory by the fantastic AIMdyn team (Dr. William Redman, Dean Huang, Dr. Maria Fonoberova, and Prof. Igor Mezic) and generously funded by the DARPA Small Business Innovation Research Program (SBIR) Program Office under Contract No. W31P4Q-21-C-0007 to AIMdyn, Inc.
https://arxiv.org/abs/2311.12615
Koopman operator theory, a data-driven dynamical systems framework, has found significant success in learning models from complex, real-world data sets, enabling state-of-the-art prediction and control. The greater interpretability and lower computational costs of these models, compared to traditional machine learning methodologies, make Koopman learning an especially appealing approach. Despite this, little work has been performed on endowing Koopman learning with the ability to learn from its own mistakes. To address this, we equip Koopman methods – developed for predicting non-stationary time-series – with an episodic memory mechanism, enabling global recall of (or attention to) periods in time where similar dynamics previously occurred. We find that a basic implementation of Koopman learning with episodic memory leads to significant improvements in prediction on synthetic and real-world data. Our framework has considerable potential for expansion, allowing for future advances, and opens exciting new directions for Koopman learning.