Reconstruction of dynamic structures of experimental setups based on measurable experimental data only

• Published in: Chinese physics B Vol. 27; no. 3; pp. 30503 - 30509
• Main Authors: Chen, Tian-Yu, Chen, Yang, Yang, Hu-Jiang, Xiao, Jing-Hua, Hu, Gang
• Format: Journal Article
• Language: English
• Published: Chinese Physical Society and IOP Publishing Ltd 01.03.2018
• Subjects: data analysis; dynamics; inverse problem
• ISSN: 1674-1056
• DOI: 10.1088/1674-1056/27/3/030503

Nowadays, massive amounts of data have been accumulated in various and wide fields, it has become today one of the central issues in interdisciplinary fields to analyze existing data and extract as much useful information as possible from data. It is often that the output data of systems are measurable while dynamic structures producing these data are hidden, and thus studies to reveal system structures by analyzing available data, i.e., reconstructions of systems become one of the most important tasks of information extractions. In the past, most of the works in this respect were based on theoretical analyses and numerical verifications. Direct analyses of experimental data are very rare. In physical science, most of the analyses of experimental setups were based on the first principles of physics laws, i.e., so-called top-down analyses. In this paper, we conducted an experiment of "Boer resonant instrument for forced vibration" (BRIFV) and inferred the dynamic structure of the experimental set purely from the analysis of the measurable experimental data, i.e., by applying the bottom-up strategy. Dynamics of the experimental set is strongly nonlinear and chaotic, and itʼs subjects to inevitable noises. We proposed to use high-order correlation computations to treat nonlinear dynamics; use two-time correlations to treat noise effects. By applying these approaches, we have successfully reconstructed the structure of the experimental setup, and the dynamic system reconstructed with the measured data reproduces good experimental results in a wide range of parameters.