Discrete Time

Discrete-time modeling focuses on representing continuous processes as sequences of events at fixed time intervals, enabling analysis and control of dynamical systems using discrete mathematical tools. Current research emphasizes developing robust and efficient algorithms for various applications, including reinforcement learning, optimal control, and system identification, often employing neural networks, stochastic approximation, and techniques like Langevin dynamics and Girsanov-based methods within these frameworks. This approach is crucial for practical applications where continuous-time models are computationally intractable or where data is inherently discrete, impacting fields such as robotics, ecology, and finance through improved model accuracy and control strategies.