Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics
This work develops a hybrid reduced-order model (ROM) called physics-informed DManD to study chaotic dynamics. It combines data-driven methods using autoencoders with physics-based projections from a full order model. The resulting models show superior predictive accuracy and robustness compared to purely data-driven models, even when the underlying physical model has errors.