Dynamic Risk Assessment

Dynamic risk assessment focuses on continuously evaluating and adapting risk estimations in complex, evolving systems. Current research emphasizes the use of machine learning, particularly deep learning models like autoencoders and convolutional neural networks, often integrated with multi-task learning and semi-supervised approaches, to improve accuracy and robustness in diverse applications such as power systems and autonomous driving. This field is crucial for enhancing safety and reliability in AI-controlled systems and other high-stakes domains, driving advancements in both model interpretability and the development of more sophisticated risk quantification methods.