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Found 3 papers, 2 papers with code
Robust Nonlinear Reduced-Order Model Predictive Control
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time.
Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifolds
We develop a methodology to construct low-dimensional predictive models from data sets representing essentially nonlinear (or non-linearizable) dynamical systems with a hyperbolic linear part that are subject to external forcing with finitely many frequencies.
Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systems
While data-driven model reduction techniques are well-established for linearizable mechanical systems, general approaches to reducing non-linearizable systems with multiple coexisting steady states have been unavailable.
