Stress Testing

Stress testing evaluates system robustness under extreme conditions, aiming to identify vulnerabilities overlooked by standard testing. Current research focuses on adapting stress testing methodologies across diverse domains, employing machine learning techniques like variational inference and reinforcement learning to generate realistic scenarios and efficiently explore failure spaces, including the use of deep learning models and digital twins. These advancements enhance the reliability and safety of complex systems, from financial portfolios and autonomous vehicles to medical image analysis and large language models, improving decision-making and risk management in critical applications.