Hidden Dynamic

Hidden dynamic research focuses on uncovering the underlying, often unobservable, processes governing complex systems' behavior from observed data. Current efforts concentrate on developing novel algorithms and model architectures, such as recurrent neural networks, state-space models, and variations of Gaussian processes, to identify these hidden dynamics, often employing techniques like variational inference and look-ahead prediction to improve accuracy and long-term forecasting. This work is crucial for advancing understanding in diverse fields, from neuroscience and ecology to improving the interpretability and robustness of machine learning models and enabling more effective control and prediction in complex systems.