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Artificial intelligence predicts nonlinear ultrafast dynamics in optics

PREIN researchers at Tampere University have successfully used artificial intelligence to predict nonlinear dynamics that take place when ultrashort light pulses interact with matter. This novel solution can be used for efficient and fast numerical modelling, for example, in imaging, manufacturing and surgery. The findings were published in the prestigious Nature Machine Intelligence journal.

Short pulse dynamics are highly nonlinear and optimizing pulse propagation for application purposes requires extensive and computationally demanding numerical simulations. This creates a severe bottleneck in designing and optimizing experiments in real time. 

The previous slow methods demanding calculations and excessive memory capasity can be replaced with artificial intelligence. Artificial intelligence can distinguish different types of laser pulse propagation. The research uses a specialized architecture known as the ‘recurrent neural network’ that possesses an internal memory.  Such a network can also learn how patterns evolve in both the temporal and spectral domains over an extended distance. In this new method a recurrent neural network is used to model and predict complex nonlinear propagation in optical fibre. In the future, algorithms embedded in laser systems can be utilized to ensure real-time optimization. 

Application are multiple from manufacturing to surgery and telecommunications to imaging. The solution will lead to more efficient and faster numerical modelling of all systems where nonlinearity influences propagation, improving the design of devices.   

The study reports two cases of highly significant interest in photonics: extreme pulse compression and ultrabroadband laser source development. 

With the rapid growth of machine learning applications in all fields of science, neural networks will very soon become an important and standard tool for analyzing complex nonlinear dynamics, for optimizing the generation of broadband sources and frequency combs, as well as for designing ultrafast optics experiments.

The research was carried out at Tampere University as part of the Applications of Artificial Intelligence in Physical Sciences and Engineering Program (AIPSE) and within the Academy of Finland’s Flagship for Photonics Research and Innovation (PREIN).

Read the article: Lauri Salmela, Nikolaos Tsipinakis, Alessandro Foi, Cyril Billet, John M. Dudley, and Goëry Genty. Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network Nature Machine Intelligence.
 
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