Distilling slow process probability density from fast random data

Author Type

Outside Researcher

Co-Author Type 1

Outside Researcher

Co-Author Type 2

Outside Researcher

Co-Author Type 3

Outside Researcher

College

Engineering and Computer Science

Department

Ocean and Mechanical Engineering

Document Type

Article

Publication/Event/Conference Title

Mechanical Systems and Signal Processing

Publication Status

Version of Record

Abstract

This paper addresses the following question: “Starting from discrete state data collected from a randomly excited dissipative nonlinear system, is it possible to directly distill the probability density of slowly varying processes hidden in it?” The answer turns out to be affirmative! This work provides a data-driven methodology to solve two successive sub-problems: extracting slow processes hidden in fast state data and identifying the probability density of slow process from the data of calculated slow process. The former resorts to a mathematical definition of slow process and an optimization algorithm, while the latter depends on the principle of maximum information entropy. The application and efficacy of this method are demonstrated by three typical nonlinear examples, i.e., two single-degree-of-freedom systems and one two-degree-of-freedom system. This method is equation-free, which circumvents stochastic differential equations and attending traditional methods. Thus, the proposed methodology is especially suitable to complex systems without derivation of associated accurate governing equations.

DOI

10.1016/j.ymssp.2022.109156

Publication Date

8-1-2022

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