Transition Function

A transition function mathematically describes how a system changes state over time, a fundamental concept across diverse scientific fields. Current research focuses on improving the accuracy and efficiency of estimating transition functions from data, employing methods ranging from metaheuristic optimization algorithms to neural networks and Fokker-Planck equations, particularly in scenarios where the underlying dynamics are unknown or complex. These advancements have significant implications for various applications, including reinforcement learning, modeling biological systems (e.g., disease progression), and improving the accuracy of predictions in dynamic systems. The ability to accurately learn and represent transition functions is crucial for building more robust and insightful models across numerous scientific disciplines.