Continuous Data Assimilation with Learned Surrogate Dynamics
Researchers have developed a new method for continuous data assimilation using learned surrogate models of dynamical systems. This approach addresses challenges where system dynamics are unknown or computationally expensive to simulate. The analysis shows that using surrogate models maintains exponential convergence, with an error floor dependent on approximation and noise levels. The study also quantifies the training data required for accurate assimilation. AI