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A damage-identification method based on flexibility matrix and Teager energy operator is proposed for low-density material–filled sandwich panels with truss core. In the proposed damage index, weight coefficient r is introduced to consider the effect of damages on both high-order and low-order modes. Numerical simulations and experiments are conducted to assess the performance of the proposed method. Effects of Young’s modulus of the filler material on the accuracy of the proposed method are also discussed. Results reveal that the method is reliable and effective for single-damage and multiple-damages identification of filled sandwich panels with truss core, and weight coefficient plays an important role, especially for cases with multiple damages or damages of small extent. Damage identification becomes more difficult as Young’s modulus of the filler increases, and there is a critical value, after which the damage could not be identified by the proposed method.

The identification of thermal load/thermal shock of aircrafts during the service is beneficial for collecting information of the service environment and avoiding risks. In the paper, a method based on multivariate information fusion and physics-guided neural network is developed for the inverse problem of thermal load identification of honeycomb sandwich structures. Two thermal feature parameters: temperature gradient and temperature variation rate are used to build the dataset. A 16-layers physics-guided neural network is presented to achieve the predicted results consistent with physical knowledge. In the work, laser irradiation is used as the thermal load, and two laser parameters are to be identified, i.e., spot diameter, power. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The effects of physics-guided loss function and multivariate information fusion are discussed, and it is found that the results based on the proposed method are much better than the results based on the method without physical model. Besides, results based on multivariate information fusion are better than results based on single temperature response. Then, the effects of network models and hyper parameters on the proposed method are also discussed.