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.

翻译：精确建模气动载荷对于理解和预测复杂结构系统的响应至关重要。然而，这些模型通常依赖于真实物理力的简化，引入的假设会限制其精度。在存在噪声或不完整数据的情况下，验证此类模型变得尤为困难。为解决此问题，我们提出了一种基于概率物理信息的机器学习方法，旨在从结构动力响应的噪声测量中重构底层气动载荷。该模型避免了过拟合，无需正则化方案，并允许在训练过程中使用异构和多保真度数据。通过在大贝尔特东桥上模拟线性非定常假设下的气动载荷重构，验证了该方法的有效性。结果显示，真实载荷与预测载荷高度一致，尤其在均方根误差、幅值、相位角和信号峰值方面表现突出。该载荷重构方法具有广泛适用性，可用于模型验证、未来载荷估算及结构损伤预测。