Aerodynamic force reconstruction using physics-informed Gaussian processes

TL;DR

This paper presents a physics-informed Gaussian process method for reconstructing aerodynamic loads from noisy structural response data, improving accuracy without overfitting and handling heterogeneous data.

Contribution

The authors develop a probabilistic machine learning approach that incorporates physical laws to accurately reconstruct aerodynamic loads from noisy, incomplete, and multi-fidelity data.

Findings

Strong agreement between true and predicted loads demonstrated on the Great Belt East Bridge.

Method effectively estimates load magnitude, phase, and peak values.

Approach is broadly applicable to validation, future load prediction, and damage prognosis.

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.