Modern optical systems increasingly rely on complex physical processes. Advanced light sources, such as supercontinuum (SC) , are highly sought for imaging and metrology, and are based on nonlinear dynamics where the output properties must often finely match target performance characteristics. However, in these systems, the availability of control parameters and the means to adjust them in a versatile manner are usually limited. Moreover, finding the ideal parameters for a specific application can become inherently complex. Here, we use an actively-controlled photonic chip to prepare and manipulate patterns of femtosecond optical pulses seeding supercontinuum generation . Taking advantage of machine learning concepts , we exploit this access to an enhanced and tunable parameter space and experimentally demonstrate the customization of nonlinear interactions responsible for tailoring supercontinuum properties .
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