  A supervised learning approach for the solution of large-scale nonlinear
stabilization problems is presented. A stabilizing feedback law is trained from
a dataset generated from State-dependent Riccati Equation solves. The training
phase is enriched by the use gradient information in the loss function, which
is weighted through the use of hyperparameters. High-dimensional nonlinear
stabilization tests demonstrate that real-time sequential large-scale Algebraic
Riccati Equation solves can be substituted by a suitably trained feedforward
neural network.
