  We propose a way to favorably employ neural networks in the field of
non-destructive testing using Full Waveform Inversion (FWI). The presented
methodology discretizes the unknown material distribution in the domain with a
neural network within an adjoint optimization. To further increase efficiency
of the FWI, pretrained neural networks are used to provide a good starting
point for the inversion. This reduces the number of iterations in the Full
Waveform Inversion for specific, yet generalizable settings.
