  Guided wave testing is a popular approach for monitoring the structural
integrity of infrastructures. We focus on the primary task of damage detection,
where signal processing techniques are commonly employed. The detection
performance is affected by a mismatch between the wave propagation model and
experimental wave data. External variations, such as temperature, which are
difficult to model, also affect the performance. While deep learning models can
be an alternative detection method, there is often a lack of real-world
training datasets. In this work, we counter this challenge by training an
ensemble of variational autoencoders only on simulation data with a wave
physics-guided adversarial component. We set up an experiment with non-uniform
temperature variations to test the robustness of the methods. We compare our
scheme with existing deep learning detection schemes and observe superior
performance on experimental data.
