  We present a deep neural network which predicts the stability of isotropic
steady states of the asymptotically flat, spherically symmetric Einstein-Vlasov
system in Schwarzschild coordinates. The network takes as input the energy
profile and the redshift of the steady state. Its architecture consists of a
U-Net with a dense bridge. The network was trained on more than ten thousand
steady states using an active learning scheme and has high accuracy on test
data. As first applications, we analyze the validity of physical hypotheses
regarding the stability of the steady states.
