  Field-induced domain wall dynamics in ferroelectric materials underpins
multiple applications ranging from actuators to information technology devices
and necessitates a quantitative description of the associated mechanisms
including giant electromechanical couplings, controlled non-linearities, or low
coercive voltages. While the advances in dynamic Piezoresponse Force Microscopy
measurements over the last two decades have rendered visualization of
polarization dynamics relatively straightforward, the associated insights into
the local mechanisms have been elusive. Here we explore the domain dynamics in
model polycrystalline materials using a workflow combining deep learning-based
segmentation of the domain structures with non-linear dimensionality reduction
using multilayer rotationally-invariant autoencoders (rVAE). The former allows
unambiguous identification and classification of the ferroelectric and
ferroelastic domain walls. The rVAE discover the latent representations of the
domain wall geometries and their dynamics, thus providing insight into the
intrinsic mechanisms of polarization switching, that can further be compared to
simple physical models. The rVAE disentangles the factors affecting the pinning
efficiency of ferroelectric walls, offering insights into the correlation of
ferroelastic wall distribution and ferroelectric wall pinning.
