| Autor: |
Kapoor T, Wang H, Nunez A, Dollevoet R |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on neural networks and learning systems [IEEE Trans Neural Netw Learn Syst] 2024 May; Vol. 35 (5), pp. 5981-5995. Date of Electronic Publication: 2024 May 02. |
| DOI: |
10.1109/TNNLS.2023.3310585 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
