Predictive reduced order modeling of chaotic multi-scale problems using adaptively sampled projections

Cheng Huang, Karthik Duraisamy

Abstract: Highlights•An adaptive reduced-order model formulation is derived to enable true predictions of chaotic and convection-dominant physics.•A compact method is formulated to update the basis to account for unresolved scales at every time step.•A full state estimation strategy is developed to incorporate non-local information in ROM adaptation.•The adaptive procedure significantly reduces the offline training cost, making it negligible compared to the online calculations.•Detailed evaluations show significant predictive accuracy in future state and parametric prediction in chaotic flows.