Stochastic Simulation

Stochastic simulation involves using computational models to represent and analyze systems with inherent randomness, aiming to understand their behavior and make predictions. Current research focuses on improving simulation efficiency through machine learning surrogates (e.g., deep convolutional networks, recurrent neural networks) and optimization techniques like evolutionary algorithms, particularly for complex applications in hydrology, breeding programs, and robotics. These advancements enable faster and more robust analyses, impacting fields like resource management, healthcare (via synthetic data generation), and the design of intelligent agents, by allowing for extensive exploration of parameter spaces and uncertainty quantification.