Transient Dynamic

Transient dynamics, the short-term behavior of systems before settling into a steady state, are a focus of intense research across diverse fields. Current investigations explore how these initial phases influence long-term outcomes, employing various models including deep neural networks, physics-informed neural networks, and reinforcement learning algorithms with distinct timescale value function decompositions. Understanding transient dynamics is crucial for improving the performance of machine learning models, optimizing control systems (e.g., in power grids), and gaining insights into complex systems like human learning and musical instrument evolution. This research has implications for enhancing the efficiency and robustness of artificial intelligence and for understanding fundamental processes in both natural and engineered systems.