Damage recovery in composite laminates through deep learning from acoustic scattering of guided waves.

We propose an innovative deep learning (DL) regression strategy combined with guided wave modes to address inverse acoustic scattering problems effectively. This approach allows for accurate recovery of heterogeneous defect fields at the interfaces of composite laminates. The neural network (NN) model's training process employs stochastic Gaussian fields as output, which are linked to the interfacial defect fields of the physical problem.