Stochastic Dynamic Model

Stochastic dynamic models are mathematical frameworks used to represent systems evolving over time under the influence of randomness. Current research focuses on developing robust control strategies for these models, particularly addressing uncertainties in parameters and noise, often employing techniques like Markov decision processes (including interval MDPs) and Bayesian inference. These advancements are crucial for improving the safety and reliability of autonomous systems and enhancing the accuracy of predictions in diverse fields, from robotics and astrophysics to quantum mechanics, by enabling more accurate modeling of complex, real-world phenomena. Efficient algorithms, including those leveraging deep learning, are being developed to overcome computational challenges associated with parameter estimation and control synthesis in these complex systems.