  In this work, ultrasonic guided waves and a dual-branch version of
convolutional neural networks are used to solve two different but related
inverse problems, i.e., finding layup sequence type and identifying material
properties. In the forward problem, polar group velocity representations are
obtained for two fundamental Lamb wave modes using the stiffness matrix method.
For the inverse problems, a supervised classification-based network is
implemented to classify the polar representations into different layup sequence
types (inverse problem - 1) and a regression-based network is utilized to
identify the material properties (inverse problem - 2)
