Full Observability

Full observability, in the context of various dynamic systems, refers to the ability to completely reconstruct a system's state from available measurements. Current research focuses on addressing challenges arising from partial observability, employing techniques like physics-informed neural networks, transformers (particularly for time series data), and various filtering and optimization methods (e.g., Kalman filtering, pose graph optimization) to improve state estimation. These advancements are crucial for enhancing the performance and reliability of applications ranging from epidemiological modeling and autonomous navigation to multi-agent systems and robotic control, where incomplete or noisy data are common. The development of robust algorithms and models that effectively handle partial observability is driving significant progress across diverse scientific and engineering domains.