Dynamical Regime

Dynamical regimes describe distinct behavioral patterns within complex systems, and research focuses on identifying, classifying, and modeling these regimes from time-series data. Current efforts utilize diverse approaches, including hierarchical and online learning methods applied to Koopman operators and neural networks (e.g., Physics-Informed Neural Networks), to capture both individual and collective dynamics across multiple regimes. Understanding and predicting transitions between these regimes is crucial for improving forecasting accuracy in various fields, from neuroscience and medicine to materials science and climate modeling, enabling more effective control and design of complex systems.