Abstract:Accurate modeling of aerodynamic loads is essential for understanding and predicting the responses of complex structural systems. However, these models often rely on simplifications of the true physical forces, introducing assumptions that can limit their accuracy. Validating such models becomes particularly challenging in the presence of noisy or incomplete data. To address this, we introduce a probabilistic physics-informed machine learning approach designed to reconstruct the underlying aerodynamic loads from noisy measurements of structural dynamic responses. The model avoids overfitting, eliminates the need for regularization schemes, and allows for the use of heterogeneous and multi-fidelity data during the training process. The efficacy of the approach is demonstrated through the reconstruction of aerodynamic loads on the Great Belt East Bridge, simulated under a linear unsteady assumption. Results show a strong agreement between true and predicted loads, particularly related to root mean squared errors, magnitude, phase angle and peak values of the signals. The method for load reconstructing holds broad applicability, such as modeling validation, future load estimation, and structural damage prognosis.
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.22111 [cs.LG] |
| (or arXiv:2605.22111v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22111 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1007/978-3-032-15130-8_20
DOI(s) linking to related resources |
From: Gledson Tondo [view email]
[v1]
Thu, 21 May 2026 07:45:19 UTC (2,484 KB)
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