  We study a one-dimensional model of a dislocation pileup driven by an
external stress and interacting with random quenched disorder, focusing on
predictability of the plastic deformation process. Upon quasistatically ramping
up the externally applied stress from zero the system responds by exhibiting an
irregular stress--strain curve consisting of a sequence of strain bursts, i.e.,
critical-like dislocation avalanches. The strain bursts are power-law
distributed up to a cutoff scale which increases with the stress level up to a
critical flow stress value. There, the system undergoes a depinning phase
transition and the dislocations start moving indefinitely, i.e., the strain
burst size diverges. Using sample-specific information about the pinning
landscape as well as the initial dislocation configuration as input, we employ
predictive models such as linear regression, simple neural networks and
convolutional neural networks to study the predictability of the simulated
stress--strain curves of individual samples. Our results show that the response
of the system -- including the flow stress value -- can be predicted quite
well, with the correlation coefficient between predicted and actual stress
exhibiting a non-monotonic dependence on strain. We also discuss our attempts
to predict the individual strain bursts.
