  We investigate the recently proposed dimensional reduction conjecture in
driven disordered systems using a machine learning technique. The conjecture
states that a static snapshot of a disordered system driven at a constant
velocity is equal to a space-time trajectory of its lower-dimensional pure
counterpart. This suggests that the three-dimensional (3D) random field XY
model exhibits the Berezinskii-Kosterlitz-Thouless transition when driven out
of equilibrium. To verify the conjecture directly by observing configurations
of the system, we utilize the capacity of neural networks to detect subtle
features of images. Specifically, we train neural networks to differentiate
snapshots of the 3D driven random field XY model from space-time trajectories
of the two-dimensional pure XY model. Our results demonstrate that the network
cannot distinguish between the two, confirming the dimensional reduction
conjecture.
