Delay Resilient

Delay resilience focuses on designing systems and algorithms that maintain functionality and performance despite unpredictable or significant delays in information transmission or processing. Current research emphasizes developing robust control strategies, often employing deep learning models like DeepONets and Transformers, and adaptive control techniques to compensate for these delays in diverse applications such as robotics, federated learning, and traffic management. This work is crucial for improving the reliability and efficiency of complex systems operating in dynamic and uncertain environments, impacting fields ranging from autonomous vehicles to supply chain optimization.