Closure Model

Closure models aim to efficiently approximate the behavior of complex systems by representing unresolved small-scale processes within a coarser-grained simulation. Current research focuses on developing data-driven closure models using neural networks, including physics-informed neural operators and Bayesian neural networks, often coupled with techniques like total variation diminishing methods to ensure physical realism. These models are crucial for accelerating simulations of diverse phenomena, from turbulent flows and climate modeling to dexterous robotic grasping, by significantly reducing computational cost while maintaining accuracy. The development of robust, generalizable, and interpretable closure models remains a key challenge and area of active investigation.