Optical Physics

Adaptive, robust, and self-tuning mode-locked lasers have eluded practical implementation for more than two decades. The ability to achieve these goals has the potential to revolutionize both the commercial and research sectors associated with ultra-fast science. We have recently demonstrated that the integration of data-driven machine learning strategies, including the use of sparse representation, with adaptive control are capable of producing an efficient and optimal self-tuning algorithm for mode- locked fiber lasers. This is a first step in automating lasers for achieving optimal performance.

Building upon self-tuning mode-locking strategies, we have recently demonstrated that machine learning methods with an extremum-seeking control protocol can self-tune a meta-material antenna to achieve high-fidelity beam steering and minimize side-lobe generation. This is critical for the emerging technologies of portable communications technologies associated with metamaterial technologies.

New analytical methods are needed now as novel pulse evolutions in lasers promise to greatly enhance the performance of practical instruments. In general, a pulse undergoes large changes in its temporal shape, spectral shape, and phase or frequency as it traverses a laser cavity, which in turn pose severe challenges to mathematical models. Highly-chirped and/or self-similar pulse solutions can exist in the presence of strong dissipation, creating new classes of laser pulses that offer remarkable behavior and performance. Understanding the stability and bifurcation of such pulse structures is fundamental for learning the performance limits and practical limits of many modern, high-intensity optical systems.