Nonlinear Oscillator

Nonlinear oscillators are dynamical systems exhibiting complex, non-repeating oscillations, studied to understand and predict their behavior in various contexts. Current research focuses on developing machine learning models, such as recurrent neural networks and physics-informed convolutional neural networks, to analyze oscillator dynamics from data, predict bifurcations, and learn flow functions. These advancements are impacting diverse fields, from robotics (e.g., creating autonomous robots with self-sustained oscillation) to machine learning (e.g., developing new algorithms for non-Euclidean data analysis) and environmental sensing (e.g., using coupled oscillators for collective underwater perception). The ability to accurately model and control nonlinear oscillators holds significant potential for advancing these and other scientific and engineering applications.