Unobservable State

Unobservable state research focuses on inferring hidden variables or processes influencing observable data, a crucial challenge across diverse fields. Current efforts leverage various techniques, including inverse optimization, diffusion models, neural ordinary differential equations, and symbolic regression, often combined with deep learning architectures to estimate or reconstruct these hidden states from incomplete or indirect measurements. This work is significant for improving model accuracy and robustness in applications ranging from electricity grid management and robotics to cognitive psychology and dynamical systems modeling, where incomplete data is common. The ultimate goal is to develop reliable methods for understanding and predicting system behavior even when key components remain unobserved.