Failure Transition

Failure transition research focuses on understanding and predicting how initial failures propagate and cascade within complex systems, aiming to improve system robustness and safety. Current research employs diverse approaches, including stochastic diffusion models (informed by information cascade principles), reinforcement learning for failure landscape exploration and mitigation, and Bayesian inference for probabilistic failure trajectory estimation. These methods are applied across various domains, from power grids and autonomous vehicles to large language models, with the goal of developing more accurate predictive models and effective mitigation strategies to prevent widespread system failures.