Physics-informed deep learning for structural dynamics under moving load

Abstract

Physics-informed deep learning has emerged as a promising approach that incorporates physical constraints into the model, reduces the amount of data required, and demonstrates robustness and potential in dealing with limited datasets for a variety of studies. However, several key challenges still exist, with one being the spectral bias problem of deep learning in the simulation of functions with multi-frequency features. To overcome the challenge, this study proposes a novel physics-informed deep learning method, which integrates physics-informed neural network with Fourier transform so as to solve partial differential equations in the frequency domain, thus alleviating the problem of spectral bias of neural networks in the simulation of multi-frequency functions. In addition, the proposed method is used to focus on the forward simulation and parameter inverse identification issues in structural dynamics under moving loads. To illustrate the superiority of the method, the issues of dynamic response of simply supported beams under moving loads are presented as case studies, and the performance of the method in multiple cases is analysed and discussed. The research results demonstrate the feasibility and effectiveness of the method for structural dynamics simulation and parameter inverse identifications using limited datasets.