Data-driven learning for the Mori-Zwanzig formalism: a generalization of the Koopman learning framework
Yen Ting Lin, Yifeng Tian, Marian Anghel, Daniel Livescu

TL;DR
This paper presents a unified theoretical framework combining Mori-Zwanzig and Koopman formalisms, develops data-driven algorithms to extract key operators, and demonstrates improved predictive capabilities using the Lorenz `96 system.
Contribution
It generalizes the Koopman learning framework to include Mori-Zwanzig formalism and introduces algorithms for data-driven extraction of operators in high-dimensional systems.
Findings
Operators exhibit complex behaviors not captured by traditional models
Numerical verification of the Generalized Fluctuation Dissipation relationship
Generalized Langevin Equation outperforms Koopman operators in predictions
Abstract
A theoretical framework which unifies the conventional Mori-Zwanzig formalism and the approximate Koopman learning is presented. In this framework, the Mori-Zwanzig formalism, developed in statistical mechanics to tackle the hard problem of construction of reduced-order dynamics for high-dimensional dynamical systems, can be considered as a natural generalization of the Koopman description of the dynamical system. We next show that similar to the approximate Koopman learning methods, data-driven methods can be developed for the Mori-Zwanzig formalism with Mori's linear projection operator. We developed two algorithms to extract the key operators, the Markov and the memory kernel, using time series of a reduced set of observables in a dynamical system. We adopted the Lorenz `96 system as a test problem and solved for the operators, which exhibit complex behaviors which are unlikely to be…
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Taxonomy
TopicsModel Reduction and Neural Networks
