  The use of ultrasonic guided waves to probe the materials/structures for
damage continues to increase in popularity for non-destructive evaluation (NDE)
and structural health monitoring (SHM). The use of high-frequency waves such as
these offers an advantage over low-frequency methods from their ability to
detect damage on a smaller scale. However, in order to assess damage in a
structure, and implement any NDE or SHM tool, knowledge of the behaviour of a
guided wave throughout the material/structure is important (especially when
designing sensor placement for SHM systems). Determining this behaviour is
extremely diffcult in complex materials, such as fibre-matrix composites, where
unique phenomena such as continuous mode conversion takes place. This paper
introduces a novel method for modelling the feature-space of guided waves in a
composite material. This technique is based on a data-driven model, where prior
physical knowledge can be used to create structured machine learning tools;
where constraints are applied to provide said structure. The method shown makes
use of Gaussian processes, a full Bayesian analysis tool, and in this paper it
is shown how physical knowledge of the guided waves can be utilised in
modelling using an ML tool. This paper shows that through careful consideration
when applying machine learning techniques, more robust models can be generated
which offer advantages such as extrapolation ability and physical
interpretation.
