The field of dynamical systems is witnessing significant developments in online learning and estimation, with a focus on handling non-independent and non-identically distributed data. Researchers are proposing innovative methods to address the challenges of persistent excitation and uncertainty in system trajectories. Notable advancements include the development of online projected Newton-type algorithms, uncertainty-aware hybrid machine learning architectures, and debiasing techniques for continuous-time nonlinear autoregressions. These innovations have the potential to improve the accuracy and reliability of system estimation and prediction, with applications in areas such as autonomous vehicles and underwater exploration.

Some noteworthy papers in this area include: The paper 'Online Learning for Nonlinear Dynamical Systems without the I.I.D. Condition' which proposes an online projected Newton-type algorithm for parameter estimation in nonlinear stochastic dynamical systems. The paper 'Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation' which presents an uncertainty-aware hybrid learning architecture for vehicle state estimation. The paper 'Debiasing Continuous-time Nonlinear Autoregressions' which introduces novel bias corrections for consistent estimation of continuous-time nonlinear systems.