Local parameter identification with neural ordinary differential equations

doi:10.1007/s10483-022-2926-9

Abstract

Abstract: The data-driven methods extract the feature information from data to build system models, which enable estimation and identification of the systems and can be utilized for prognosis and health management (PHM). However, most data-driven models are still black-box models that cannot be interpreted. In this study, we use the neural ordinary differential equations (ODEs), especially the inherent computational relationships of a system added to the loss function calculation, to approximate the governing equations. In addition, a new strategy for identifying the local parameters of the system is investigated, which can be utilized for system parameter identification and damage detection. The numerical and experimental examples presented in the paper demonstrate that the strategy has high accuracy and good local parameter identification. Moreover, the proposed method has the advantage of being interpretable. It can directly approximate the underlying governing dynamics and be a worthwhile strategy for system identification and PHM.

Key words: neural ordinary differential equation (ODE), parameter identification, prognosis and health management (PHM), system damage detection

2010 MSC Number:

34C15
93B30

Qiang YIN, Juntong CAI, Xue GONG, Qian DING. Local parameter identification with neural ordinary differential equations. Applied Mathematics and Mechanics (English Edition), 2022, 43(12): 1887-1900.

TrendMD

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