Local parameter identification with neural ordinary differential equations |
| |
Authors: | Qiang YIN Juntong CAI Xue GONG Qian DING |
| |
Affiliation: | Department of Mechanics, Tianjin University, Tianjin 300350, China |
| |
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. |
| |
Keywords: | neural ordinary differential equation (ODE) parameter identification prognosis and health management (PHM) system damage detection |
|
| 点击此处可从《应用数学和力学(英文版)》浏览原始摘要信息 |
|
点击此处可从《应用数学和力学(英文版)》下载全文 |
|