Spectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study.

Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase of the input pulses, different optimization approaches including a genetic algorithm, particle swarm optimizer, and simulated annealing.

Optimization and realignment of OAM mode excitation in ring-core optical fibers using machine learning.

Light beams carrying orbital angular momentum (OAM) in free space or within optical fibers have a wide range of applications in optics; however, exciting these modes with both high purity and low loss generally requires demanding optimization of excitation conditions in a high dimensional space. Furthermore, mechanical drift can significantly degrade the mode purity over time, which may limit practical deployment of OAM modes in concrete applications. Here, combining an iterative wavefront matching approach and a genetic algorithm, we demonstrate rapid and automated excitation of OAM modes with optimized purity and reduced loss.

Into the Blue: Exploring genetic mechanisms behind the evolution of baleen whales.

Marine ecosystems are ideal for studying evolutionary adaptations involved in lineage diversification due to few physical barriers and reduced opportunities for strict allopatry compared to terrestrial ecosystems. Cetaceans (whales, dolphins, and porpoises) are a diverse group of mammals that successfully adapted to various habitats within the aquatic environment around 50 million years ago. While the overall adaptive transition from terrestrial to fully aquatic species is relatively well understood, the radiation of modern whales is still unclear.

Automating physical intuition in nonlinear fiber optics with unsupervised dominant balance search.

Identifying the underlying processes that locally dominate physical interactions is the key to understanding nonlinear dynamics. Machine-learning techniques have recently been shown to be highly promising in automating the search for dominant physics, adding important insights that complement analytical methods and empirical intuition. Here we apply a fully unsupervised approach to the search for dominant balance during nonlinear and dispersive propagation in an optical fiber and show that we can algorithmically identify dominant interactions in cases of optical wavebreaking, soliton fission, dispersive wave generation, and Raman soliton emergence.

Supercontinuum generation in a graded-index multimode tellurite fiber.

We report the generation of a broadband supercontinuum (SC) from 790 to 2900 nm in a tellurite graded-index (GRIN) multimode fiber with a nanostructured core. We study the SC dynamics in different dispersion regimes and observe near-single-mode spatial intensity distribution at high input energy values. Numerical simulations of the (3 + 1)D generalized nonlinear Schrödinger equation are in good agreement with our experiments.