Rare Transition

Rare transition research focuses on efficiently modeling and predicting infrequent events across diverse fields, from climate modeling to molecular dynamics and reinforcement learning. Current efforts concentrate on developing advanced algorithms, including those based on machine learning (e.g., neural networks, reservoir computing) and reinforcement learning, to accurately estimate transition probabilities and pathways, often employing techniques like importance sampling and splitting methods to overcome computational challenges. These advancements are crucial for improving the accuracy of simulations, optimizing decision-making in complex systems, and gaining deeper insights into the dynamics of various phenomena.