This article proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Bernoulli and Timoshenko theories, where the double beams are connected with a Winkler foundation. In particular, forward and inverse problems for the Euler-Bernoulli and Timoshenko partial differential equations (PDEs) are solved using nondimensional equations with the physics-informed loss function. Higher order complex beam PDEs are efficiently solved for forward problems to compute the transverse displacements and cross-sectional rotations with less than $1e-3$ % error. Furthermore, inverse problems are robustly solved to determine the unknown dimensionless model parameters and applied force in the entire space-time domain, even in the case of noisy data. The results suggest that PINNs are a promising strategy for solving problems in engineering structures and machines involving beam systems.
CITATION STYLE
Kapoor, T., Wang, H., Nunez, A., & Dollevoet, R. (2024). Physics-Informed Neural Networks for Solving Forward and Inverse Problems in Complex Beam Systems. IEEE Transactions on Neural Networks and Learning Systems, 35(5), 5981–5995. https://doi.org/10.1109/TNNLS.2023.3310585
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