Discretization and neural network architecture. (a) Our discrete-time model aims to predict the shape of the mirror at a time Δt in the future (st+1) using a finite history of mirror shapes and voltages input to actuators. Note that st−3 and vt+1 are not used in the prediction of st+1. (b) Our learned system dynamics model consists of five fully connected (FC) layers ([input dimension, output dimension]) followed by exponential linear unit (ELU) activation functions. Additionally, a skip connection was introduced which greatly improved its ability to predict when the mirror was at or close to rest.