Monitoring aircraft structural health with changing loads is critical. This paper presents a deep learning-based aircraft load model for strain prediction and load model calibration using a two-phase process. The model achieves 97.16% prediction accuracy and 99.49% goodness-of-fit using 2 million flight recording data points. This reduces ground testing needs and improves load prediction accuracy.
