Adapting optimal control theory and using learning loops to provide experimentally feasible shaping mask patterns

In this article we present two algorithms that provide optimal control pulses directly applicable in experiment. These are, namely, the optimal control algorithm, in which we incorporated spectral pressure during optimization, and complementarily a theoretical variant of the learning loop based on an evolutionary algorithm. With these tools a productive dialog between theory and experiments on optimal control is imaginable. The implementation of these pulses is assured by calculating the optimal mask pattern, that serves as an interface between theory and experiment.