Machine learning algorithms predict experimental output of chaotic lasers

MYRIAM E. NONAKA; MÓNICA B. AGÜERO; MARCELO G. KOVALSKY

OPTICS LETTERS

OPTICAL SOC AMER

Lugar: Washington; Año: 2023 vol. 48 p. 1060 - 1063

0146-9592

We apply an artificial neural network (ANN) of 20 hidden layers and backpropagation regression to the forecast of experimental time series from a KLM Ti:Sapphire laser and a Nd:Vanadate with modulation losses. In both cases the neural network is able to predict up to 10 steps ahead. In the Ti:Sa laser the prediction in pulse amplitude is accurate even when the pulse is an extreme event. In the Nd:Vanadate laser we forecast both pulse amplitude and pulse to pulse time separation. In both cases the prediction goes beyond the Lyapunov prediction horizon.