Disentangled representation learning with physics-informed variational autoencoder for structural health monitoring

Manual inspection and assessment of structures on a large scale is labour intensive and often infeasible, while data-driven machine learning techniques can fail to identify relevant failure mechanisms and suffer from poor generalization to previously unseen conditions, particularly when limited information is available. We propose a physics-informed variational autoencoder formulation for disentangled representation learning of confounding sources in the measurements with the aim of computing the posterior distribution of latent parameters of a physics-based model and predicting the response of a structure when limited measurements are available. The latent space of the autoencoder is augmented with a set of physics-based latent variables that are interpretable and allow for domain knowledge in the form of prior distributions and physics-based models to be included in the autoencoder formulation. To prevent the data-driven components of the model from overriding the known physics, a regularization term is included in the training objective that imposes constraints on the latent space and the generative model prediction. The feasibility of the proposed approach is evaluated on a synthetic case study.