  Ultrasonic guided waves are commonly used to localize structural damage in
infrastructures such as buildings, airplanes, bridges. Damage localization can
be viewed as an inverse problem. Physical model based techniques are popular
for guided wave based damage localization. The performance of these techniques
depend on the degree of faithfulness with which the physical model describes
wave propagation. External factors such as environmental variations and random
noise are a source of uncertainty in wave propagation. The physical modeling of
uncertainty in an inverse problem is still a challenging problem. In this work,
we propose a deep learning based model for robust damage localization in
presence of uncertainty. Wave data with uncertainty is simulated to reflect
variations due to external factors and Gaussian noise is added to reflect
random noise in the environment. After evaluating the localization error on
test data with uncertainty, we observe that the deep learning model trained
with uncertainty can learn robust representations. The approach shows potential
for dealing with uncertainty in physical science problems using deep learning
models.
