Stochastic System

Stochastic systems research focuses on understanding and controlling systems exhibiting inherent randomness, aiming to predict their behavior and optimize their performance under uncertainty. Current research emphasizes developing data-driven methods for modeling and verifying these systems, employing techniques like Gaussian processes, neural networks, and Markov chains, often within frameworks of robust control and probabilistic safety guarantees. These advancements are crucial for addressing challenges in diverse fields, including robotics, autonomous systems, and operations research, where reliable predictions and safety assurances are paramount. The development of efficient algorithms for inference, control, and verification of stochastic systems with complex dynamics remains a central theme.