Stochastic Model

Stochastic models are mathematical frameworks used to represent systems with inherent randomness, aiming to capture their probabilistic behavior and predict future states. Current research emphasizes developing and analyzing these models across diverse fields, focusing on areas like efficient algorithms for optimization in adversarial settings, the use of neural networks for surrogate modeling and uncertainty quantification in complex systems (e.g., atmospheric dynamics, financial modeling), and the development of novel architectures for learning stochastic dynamics from data. The ability to accurately model and predict stochastic processes has significant implications for various scientific disciplines and practical applications, ranging from improved weather forecasting and risk assessment to more robust machine learning algorithms.