Partially Observed

Partially observed systems, where only incomplete information about a system's state is available, pose significant challenges for modeling and control. Current research focuses on developing data-driven methods, employing neural networks (like recurrent equilibrium networks and echo-state networks) and hybrid models combining neural networks with symbolic regression or domain-specific knowledge, to learn governing equations and effective control policies from limited data. These advancements are crucial for tackling real-world problems where complete state information is often unavailable, impacting fields like robotics, climate modeling, and financial forecasting. The development of provably efficient algorithms and stability guarantees for these methods is a key area of ongoing investigation.