New control techniques are required to utilize the full potential of next generation high-energy high-repetition-rate pulses lasers while ensuring their safe operation. During automated optimization of an experiment, the control system is required to identify and reject unsafe laser configurations proposed by the optimizer. Using conventional physics codes render impossible when applied to a high energy laser system with 1ms or less time between shots, and also including laser fluctuations and drift. To mitigate this, we are using a deep Bayesian neural network to map the laser’s input power spectrum to its output power spectrum and demonstrate the speed of this approach. The Bayesian neural network can provide an estimate of its own uncertainty as a function of wavelength. A recently developed algorithm enables the uncertainty to be calculated inexpensively using multiple dropout layers inserted into the model. The uncertainty estimates are used by an active learning algorithm to improve the accuracy of the model and intelligently explore the input domain.
A laser system made of two beams of 10 PW each has been designed and is currently built for ELI-NP research infrastructure. Design is presented as well as preliminary results up to the 1PW level amplifier.