@inproceedings{ae7e55f02fee42038a39663d221d5c5e,
title = "Physics-Informed Neural Network for Nonlinear Structural System Identification",
abstract = "Structural system identification is a critical task in resilience assessments, especially following a natural hazard. In this paper, we propose PIDynNet, a novel physics-informed approach that produces an ordinary differential equation-constrained neural network model for structural system identification. PIDynNet improves the estimation of structural parameters of nonlinear structural systems by embedding an auxiliary physics-based loss term into the overall loss function as well as a supervised data-driven loss term. The proposed framework has the generalization capability to predict nonlinear structural response given unseen ground excitations. Two nonlinear numerical experiments are conducted to demonstrate the advantage of PIDynNet over other identification methods in problems with or without latent variables.",
author = "Tong Liu and Hadi Meidani",
note = "Publisher Copyright: {\textcopyright} 2023 by DEStech Publi cations, Inc. All rights reserved; 14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023 ; Conference date: 12-09-2023 Through 14-09-2023",
year = "2023",
language = "English (US)",
series = "Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring",
publisher = "DEStech Publications",
pages = "3001--3011",
editor = "Saman Farhangdoust and Alfredo Guemes and Fu-Kuo Chang",
booktitle = "Structural Health Monitoring 2023",
}