Failure Probability

Failure probability estimation is crucial for assessing the reliability of systems across diverse fields, from engineering and manufacturing to autonomous vehicles and machine learning. Current research focuses on developing efficient algorithms, such as adaptive sequential sampling, Bayesian optimization, and reinforcement learning techniques, to accurately estimate failure probabilities, especially in scenarios with rare events or high-dimensional input spaces. These advancements address challenges posed by computationally expensive simulations, limited data, and the need for robust risk assessment in high-stakes applications. Improved failure probability estimation methods directly enhance the safety and reliability of complex systems, leading to more informed decision-making and improved system design.