Evolving System

Evolving systems research focuses on understanding and modeling systems that change over time, aiming to improve prediction, adaptation, and control in dynamic environments. Current research employs diverse approaches, including graph neural networks, recurrent neural networks, and evolutionary algorithms, often coupled with uncertainty quantification methods like Stein variational gradient descent. This field is significant for its applications in diverse areas such as network management, software engineering, and biological modeling, offering improved system resilience and autonomous adaptation capabilities.