多物理
非线性系统
非线性系统辨识
系统标识
鉴定(生物学)
人工神经网络
物理
结构体系
期限(时间)
复杂系统
统计物理学
控制工程
计算机科学
人工智能
工程类
有限元法
数据建模
结构工程
生物
热力学
植物
量子力学
数据库
出处
期刊:Journal of Engineering Mechanics-asce
[American Society of Civil Engineers]
日期:2023-08-03
卷期号:149 (10)
被引量:11
标识
DOI:10.1061/jenmdt.emeng-7060
摘要
Structural system identification is critical in resilience assessments and structural health monitoring, especially following natural hazards. Among the nonlinear structural behaviors, structural damping is a complex behavior that can be modeled as a multiphysics system wherein the structure interacts with an external thermal bath and undergoes thermalization. In this paper, we propose a novel physics-informed neural network approach for nonlinear structural system identification and demonstrate its application in multiphysics cases where the damping term is governed by a separated dynamics equation. The proposed approach, called PIDynNet, improves the estimation of the parameters of nonlinear structural systems by integrating auxiliary physics-based loss terms, one for the structural dynamics and one for the thermal transfer. These physics-based loss terms form the overall loss function in addition to a supervised data-based loss term. To ensure effective learning during the identification process, subsampling and early stopping strategies are developed. The proposed framework also has the generalization capability to predict nonlinear responses for unseen ground excitations. Two numerical experiments of nonlinear systems are conducted to demonstrate the comparative performance of PIDynNet.
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