Sub-field · 3 papers
Agent-Based Model Data Assimilation
Integrating data assimilation techniques—particularly particle filters and ensemble methods—with agent-based models and dynamical systems is the central focus, aiming to calibrate simulations against real-time observations and quantify uncertainty. Applications range from crowd simulation to broader dynamical systems, with machine learning increasingly combined with data assimilation to improve state estimation and prediction.