用物理约束的高斯过程重建气动载荷，提升结构响应预测精度。
Aerodynamic force reconstruction using physics-informed Gaussian processes
- 融合物理规律的高斯过程模型，自动处理噪声与数据不完整问题。
- 在大贝尔东桥案例中，重构载荷与真实值误差极小，峰值、相位均高度吻合。
- 适合结构健康监测、载荷预测与模型验证，无需额外正则化。
精确建模气动载荷对理解复杂结构系统的响应至关重要。然而，现有模型常依赖物理力的简化假设，限制了准确性，尤其在存在噪声或数据缺失时更难验证。为此，本文提出一种概率性物理信息机器学习方法，可从结构动态响应的噪声测量中重建底层气动载荷。该模型避免过拟合，无需正则化，支持异质与多保真度数据训练。在大贝尔东桥线性非稳态假设下的仿真中，重构结果与真实载荷高度一致，根均方误差、幅值、相位角及峰值均表现优异。该方法适用于模型验证、未来载荷预测与结构损伤诊断，具有广泛应用潜力。
原文摘要 · Abstract (English)
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.
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